Handling bulk requests for resources

ABSTRACT

Some examples describes herein relate to handling bulk requests for resources. In one example, a system can determine a bulk request parameter-value associated with a bulk request. The system can then predict a baseline benefit value, which can be a benefit value when the bulk request parameter-value is used as a lower boundary for a unit parameter-value. The system can also determine a lower boundary constraint on the unit parameter-value independently of the bulk request parameter-value. The system can then execute an iterative process using the baseline benefit value and the lower boundary constraint. Based on a result of the iterative process, the system can determine whether and how much the bulk request parameter-value should be adjusted. The system may adjust the bulk request parameter-value accordingly or output a recommendation to do so.

REFERENCE TO RELATED APPLICATIONS

This claims the benefit of priority under 35 U.S.C. § 119(e) to U.S.Provisional Patent Application No. 63/223,065, filed Jul. 19, 2021, theentirety of which is hereby incorporated by reference herein.

TECHNICAL FIELD

The present disclosure relates generally to handling resource requests.More specifically, but not by way of limitation, this disclosure relatesto handling bulk requests for resources, such as virtual machines in acloud computing environment.

BACKGROUND

Cloud service providers and other service providers may provide entitieswith access to products or services, which can be supplied as units thatcan be bought or leased individually or in groups. Examples of the unitscan include cloud resources such as processors, memory, storage space,virtual machines, microservices, and serverless functions; spaces suchas lodging or offices; and equipment such as tractors, robots, turbines,and pumps. There is typically a finite number of such units available.In some cases, an entity may make a bulk request to reserve a group ofunits during a target timeframe. Such bulk requests may be made well inadvance of the target timeframe, such as months or years in advance.Other entities may make ad-hoc requests for units closer to, or during,the timeframe when the units are needed.

SUMMARY

One example of the present disclosure includes a system that has one ormore processors and one or more memory devices including program codethat is executable by the one or more processors for causing the one ormore processors to perform operations. The operations can includesetting a boundary reference value to a first default value in memory;setting a gain reference value to a second default value in the memory;and determining a bulk request parameter-value assigned to a bulkrequest, wherein the bulk request is for reserving a group of unitsduring a selected timeframe, and wherein the bulk requestparameter-value is a value for a parameter assigned to the bulk request.The operations can include predicting a baseline benefit value based onthe bulk request parameter-value, the baseline benefit value beingpredicted by using the bulk request parameter-value as a lower boundaryfor a unit parameter-value, the unit parameter-value being another valuefor the parameter assigned to an individual unit during the selectedtimeframe. The operations can include determining a lower boundaryconstraint for the unit parameter-value independently of the bulkrequest parameter-value. The operations can include executing aniterative process. The iterative process can involve (a) determining acandidate parameter-value for the parameter based on the lower boundaryconstraint; (b) determining a new gain value based on the candidateparameter-value and the baseline benefit value; (c) in response todetermining that the new gain value is greater than the gain referencevalue: setting the gain reference value to the new gain value; andsetting the boundary reference value to the lower boundary constraint;(d) in response to determining that the candidate parameter-value isless than a threshold value: updating the lower boundary constraintbased on the candidate parameter-value; and returning to operation (a);and (e) in response to determining that the candidate parameter-value isgreater than or equal to the threshold value: exiting the iterativeprocess. The operations can include, subsequent to exiting the iterativeprocess, determine whether the gain reference value is greater thanzero. The operations can include, based on determining that the gainreference value is greater than zero, adjust the bulk requestparameter-value to the boundary reference value.

Another example of the present disclosure includes a non-transitorycomputer-readable medium comprising program code that is executable byone or more processors for causing the one or more processors to performoperations. The operations can include setting a boundary referencevalue to a first default value in memory; setting a gain reference valueto a second default value in the memory; and determining a bulk requestparameter-value assigned to a bulk request, wherein the bulk request isfor reserving a group of units during a selected timeframe, and whereinthe bulk request parameter-value is a value for a parameter assigned tothe bulk request. The operations can include predicting a baselinebenefit value based on the bulk request parameter-value, the baselinebenefit value being predicted by using the bulk request parameter-valueas a lower boundary for a unit parameter-value, the unit parameter-valuebeing another value for the parameter assigned to an individual unitduring the selected timeframe. The operations can include determining alower boundary constraint for the unit parameter-value independently ofthe bulk request parameter-value. The operations can include executingan iterative process. The iterative process can involve (a) determininga candidate parameter-value for the parameter based on the lowerboundary constraint; (b) determining a new gain value based on thecandidate parameter-value and the baseline benefit value; (c) inresponse to determining that the new gain value is greater than the gainreference value: setting the gain reference value to the new gain value;and setting the boundary reference value to the lower boundaryconstraint; (d) in response to determining that the candidateparameter-value is less than a threshold value: updating the lowerboundary constraint based on the candidate parameter-value; andreturning to operation (a); and (e) in response to determining that thecandidate parameter-value is greater than or equal to the thresholdvalue: exiting the iterative process. The operations can include,subsequent to exiting the iterative process, determine whether the gainreference value is greater than zero. The operations can include, basedon determining that the gain reference value is greater than zero,adjust the bulk request parameter-value to the boundary reference value.

Yet another example of the present disclosure includes a method that canbe performed by one or more processors. The method can include setting aboundary reference value to a first default value in memory; setting again reference value to a second default value in the memory; anddetermining a bulk request parameter-value assigned to a bulk request,wherein the bulk request is for reserving a group of units during aselected timeframe, and wherein the bulk request parameter-value is avalue for a parameter assigned to the bulk request. The method caninclude predicting a baseline benefit value based on the bulk requestparameter-value, the baseline benefit value being predicted by using thebulk request parameter-value as a lower boundary for a unitparameter-value, the unit parameter-value being another value for theparameter assigned to an individual unit during the selected timeframe.The method can include determining a lower boundary constraint for theunit parameter-value independently of the bulk request parameter-value.The method can include executing an iterative process. The iterativeprocess can involve (a) determining a candidate parameter-value for theparameter based on the lower boundary constraint; (b) determining a newgain value based on the candidate parameter-value and the baselinebenefit value; (c) in response to determining that the new gain value isgreater than the gain reference value: setting the gain reference valueto the new gain value; and setting the boundary reference value to thelower boundary constraint; and (d) in response to determining that thecandidate parameter-value is less than a threshold value: updating thelower boundary constraint based on the candidate parameter-value; andreturning to operation (a). The method can include exiting the iterativeprocess. The method can include, subsequent to exiting the iterativeprocess, determine whether the gain reference value is greater thanzero. The method can include, based on determining that the gainreference value is greater than zero, adjust the bulk requestparameter-value to the boundary reference value.

This summary is not intended to identify key or essential features ofthe claimed subject matter, nor is it intended to be used in isolationto determine the scope of the claimed subject matter. The subject mattershould be understood by reference to appropriate portions of the entirespecification, any or all drawings, and each claim.

The foregoing, together with other features and examples, will becomemore apparent upon referring to the following specification, claims, andaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is described in conjunction with the appendedfigures:

FIG. 1 depicts a block diagram of an example of a computing systemaccording to some aspects.

FIG. 2 depicts an example of devices that can communicate with eachother over an exchange system and via a network according to someaspects.

FIG. 3 depicts a block diagram of a model of an example of acommunications protocol system according to some aspects.

FIG. 4 depicts a hierarchical diagram of an example of a communicationsgrid computing system including a variety of control and worker nodesaccording to some aspects.

FIG. 5 depicts a flow chart of an example of a process for adjusting acommunications grid or a work project in a communications grid after afailure of a node according to some aspects.

FIG. 6 depicts a block diagram of a portion of a communications gridcomputing system including a control node and a worker node according tosome aspects.

FIG. 7 depicts a flow chart of an example of a process for executing adata analysis or processing project according to some aspects.

FIG. 8 depicts a block diagram including components of an Event StreamProcessing Engine (ESPE) according to some aspects.

FIG. 9 depicts a flow chart of an example of a process includingoperations performed by an event stream processing engine according tosome aspects.

FIG. 10 depicts a block diagram of an ESP system interfacing between apublishing device and multiple event subscribing devices according tosome aspects.

FIG. 11 is a flow chart of an example of a process for generating andusing a machine-learning model according to some aspects.

FIG. 12 is a node-link diagram of an example of a neural networkaccording to some aspects.

FIG. 13 shows a block diagram of an example of a system for handlingbulk requests and ad-hoc requests according to some aspects of thepresent disclosure.

FIG. 14 shows a flow chart of an example of a process for adjusting abulk request parameter-value according to some aspects of the presentdisclosure.

FIG. 15 shows a flow chart of an example of an iterative processaccording to some aspects of the present disclosure.

FIG. 16 shows an example of a graphical user interface according to someaspects of the present disclosure.

FIG. 17 shows a flow chart of an example of a process for facilitatingthe creation and usage of an interactive user interface according tosome aspects of the present disclosure.

In the appended figures, similar components or features can have thesame reference label. Further, various components of the same type canbe distinguished by following the reference label with a lowercaseletter that distinguishes among the similar components. If only thefirst reference label is used in the specification, the description isapplicable to any one of the similar components having the same firstreference label irrespective of the lowercase letter.

DETAILED DESCRIPTION

In various industries, users can issue bulk requests and ad-hoc requeststo access units provided by a service provider. Service providers oftenprefer bulk requests, because bulk requests are generally provided wellin advance of a selected timeframe in which the units are needed andguarantee a certain amount of resource usage during that timeframe. Forexample, a cloud service provider may prefer bulk requests for virtualmachines over ad-hoc requests, because the bulk requests allow theservice provider to better predict and manage loading on the cloudcomputing system.

To help incentivize bulk requests, a service provider may guarantee abulk requestor that a parameter value assigned to the bulk request willbe more desirable than the parameter value assigned to individual unitsduring the selected timeframe. As one example, a bulk requestor maysubmit a request to a cloud service provider to reserve a group ofvirtual machines during a selected timeframe. In return, the cloudservice provider may guarantee the bulk requestor that a parameter valueassigned to the bulk request will be more preferable than the parametervalue assigned to individual virtual machines during that sametimeframe. Examples of the parameter can include a quality of service(QoS) parameter, cost parameter, priority parameter, network bandwidthparameter, memory parameter, storage parameter, or latency parameter.The value of a parameter value assigned to the bulk request can bereferred to as the bulk request parameter-value. The parameter valueassigned to individual units during the selected timeframe can bereferred to as the unit parameter-value. Due to this guarantee, the bulkrequest parameter-value can constrain (e.g., serve as a minimum ormaximum for) the unit parameter-value during the selected timeframe.

In some cases, the abovementioned constraints can result in suboptimalresource usage or another suboptimal result. For example, theseconstraints may make it less desirable for users to access individualunits from the service provider during the selected timeframe. So, usersmay use other service providers to handle their ad-hoc requests. Thismay result in underutilization of the original service provider'ssystem. For instance, a bulk requestor may request a certain amount ofcloud storage space from a cloud provider during a selected timeframe.In exchange, the cloud provider may give the bulk requestor a guaranteeof the type described above. Due to this guarantee, the cloud providermay impose constraints on the parameter values for individual units ofcloud storage space during the selected timeframe. The constraints maydeter users that want to use the cloud provider's system on a moread-hoc basis. As a result, those users may turn to other serviceproviders to fulfill their requests, thereby causing the original cloudprovider to have cloud storage space that goes unused. Underutilizationof the cloud provider's system can have significant negative impacts onboth the cloud provider and its users. For example, the system may notfunction properly or optimally if certain loading conditions are notmet.

For the above reasons, system operators (e.g., individuals operatingsome aspect of the system) may monitor the impact of bulk requests in aneffort to optimize resource usage or obtain another desired result. Thismay be facilitated using predictive software that can predict the numberof ad-hoc requests likely to be received in relation to a targettimeframe, based on user-specified parameter values. The system operatorcan execute the predictive software on a computer to determine whether aguarantee issued to a bulk requestor will negatively impact a targetmetric such as resource usage during a target timeframe (e.g., becauseit will significantly decrease the number of ad-hoc requests received inrelation to the target timeframe). If so, the system operator may takeaction to reduce this negative impact. For example, the system operatormay communicate with the bulk requestor to modify their arrangement in away that yields an increased number of ad-hoc requests during the targettimeframe, thereby increasing resource usage. Modifying the arrangementmay involve adjusting the bulk request parameter-value so as to loosenthe resultant constraints on the unit parameter-value. In more extremecircumstances, modifying the arrangement may involve retracting theguarantee altogether, but it is generally preferable for the systemoperator to work with the bulk requestor to adjust the bulk requestparameter-value.

It can be challenging to determine whether and how much to adjust thebulk request parameter-value to achieve optimal resource usage oranother desired result. For example, a user may need to repeatedlyprovide manual inputs to the predictive software and assess the resultsthereof to determine how to adjust a bulk request parameter-value toimprove resource usage or achieve another desired result. This islargely a manual, subjective, slow, difficult, and tedious process thatmay require industry-specific expertise. Additionally, this processrequires repeatedly executing the predictive software on a computer,which can consume significant amounts of computational power, electricalpower, and computing resources such as memory and storage space.

Some examples of the present disclosure can overcome one or more of theabovementioned problems by providing a system that automaticallyexecutes an iterative process configured to identify a bulk requestparameter-value that achieves a desired improvement, such as increasedresource usage. More specifically, the system can determine a parametervalue assigned to a bulk request for reserving a group of units during aselected timeframe. The system can then determine a benefit valueassociated with the scenario in which parameter values for individualunits are constrained by the bulk request parameter-value. This benefitvalue can be referred to as a baseline benefit value. The baselinebenefit value can quantify the benefit to a service provider of havingthe unit parameter-value constrained by the bulk requestparameter-value. The system can also determine benefit values associatedwith the scenario in which the unit parameter-value is not constrainedby the bulk request parameter-value. This can be achieved by executingan iterative process. In each iteration of the iterative process, thesystem can adjust the unit parameter-value in a way that isunconstrained by the bulk request parameter-value, determine whether theadjusted parameter value results in a benefit gain that is greater thanwas achieved in prior iterations and, if so, store the benefit gain. Thebenefit gain can quantify the amount of benefit there is to the serviceprovider relative to the baseline benefit value. This iterative processcan continue until a stopping condition is met, at which point theiterative process can end.

Once the iterative process ends, the system can determine whether thestored benefit gain is positive or negative. If the stored benefit gainis positive, it may mean that there is a greater benefit to the serviceprovider in adjusting the bulk request parameter-value than in leavingthe bulk request parameter-value as is. So, the system can generate arecommendation or execute an automated process to modify the bulkrequest parameter-value. The bulk request parameter-value can bemodified to be equivalent to the unit parameter-value that yielded thestored benefit gain. If the stored benefit gain is negative, it may meanthat there is a greater benefit to the service provider in leaving thebulk request parameter-value as is. So, the system can generate arecommendation to maintain the bulk request parameter-value as is.

Using the above techniques, the system can automatically determinewhether the bulk request parameter-value should be adjusted as well as anew value for the bulk request parameter-value that achieves an improvedresult. The new value may be considered an optimal value determinedthrough this optimization process. The system can then output arecommendation, or adjust the bulk request parameter-value, to achievethe improved result. For example, the system may automatically adjustthe bulk request parameter-value to the new value, which can result inthe receipt of a larger number of ad-hoc requests thereby increasingresource usage in a cloud computing environment.

These illustrative examples are given to introduce the reader to thegeneral subject matter discussed here and are not intended to limit thescope of the disclosed concepts. The following sections describe variousadditional features and examples with reference to the drawings in whichlike numerals indicate like elements but, like the illustrativeexamples, should not be used to limit the present disclosure.

FIGS. 1-12 depict examples of systems and methods usable for handlingbulk requests and ad-hoc requests according to some aspects. Forexample, FIG. 1 is a block diagram of an example of the hardwarecomponents of a computing system according to some aspects. Datatransmission network 100 is a specialized computer system that may beused for processing large amounts of data where a large number ofcomputer processing cycles are required.

Data transmission network 100 may also include computing environment114. Computing environment 114 may be a specialized computer or othermachine that processes the data received within the data transmissionnetwork 100. The computing environment 114 may include one or more othersystems. For example, computing environment 114 may include a databasesystem 118 or a communications grid 120. The computing environment 114can include one or more processing devices (e.g., distributed over oneor more networks or otherwise in communication with one another) thatmay be collectively be referred to herein as a processor or a processingdevice.

Data transmission network 100 also includes one or more network devices102. Network devices 102 may include client devices that can communicatewith computing environment 114. For example, network devices 102 maysend data to the computing environment 114 to be processed, may sendcommunications to the computing environment 114 to control differentaspects of the computing environment or the data it is processing, amongother reasons. Network devices 102 may interact with the computingenvironment 114 through a number of ways, such as, for example, over oneor more networks 108.

In some examples, network devices 102 may provide a large amount ofdata, either all at once or streaming over a period of time (e.g., usingevent stream processing (ESP)), to the computing environment 114 vianetworks 108. For example, the network devices 102 can transmitelectronic messages all at once, or streaming over a period of time, tothe computing environment 114 via networks 108.

The network devices 102 may include network computers, sensors,databases, or other devices that may transmit or otherwise provide datato computing environment 114. For example, network devices 102 mayinclude local area network devices, such as routers, hubs, switches, orother computer networking devices. These devices may provide a varietyof stored or generated data, such as network data or data specific tothe network devices 102 themselves. Network devices 102 may also includesensors that monitor their environment or other devices to collect dataregarding that environment or those devices, and such network devices102 may provide data they collect over time. Network devices 102 mayalso include devices within the internet of things, such as deviceswithin a home automation network. Some of these devices may be referredto as edge devices, and may involve edge-computing circuitry. Data maybe transmitted by network devices 102 directly to computing environment114 or to network-attached data stores, such as network-attached datastores 110 for storage so that the data may be retrieved later by thecomputing environment 114 or other portions of data transmission network100. For example, the network devices 102 can transmit data to anetwork-attached data store 110 for storage. The computing environment114 may later retrieve the data from the network-attached data store 110and use the data for handling bulk requests and ad-hoc requests.

Network-attached data stores 110 can store data to be processed by thecomputing environment 114 as well as any intermediate or final datagenerated by the computing system in non-volatile memory. But in certainexamples, the configuration of the computing environment 114 allows itsoperations to be performed such that intermediate and final data resultscan be stored solely in volatile memory (e.g., RAM), without arequirement that intermediate or final data results be stored tonon-volatile types of memory (e.g., disk). This can be useful in certainsituations, such as when the computing environment 114 receives ad hocqueries from a user and when responses, which are generated byprocessing large amounts of data, need to be generated dynamically(e.g., on the fly). In this situation, the computing environment 114 maybe configured to retain the processed information within memory so thatresponses can be generated for the user at different levels of detail aswell as allow a user to interactively query against this information.

Network-attached data stores 110 may store a variety of different typesof data organized in a variety of different ways and from a variety ofdifferent sources. For example, network-attached data stores may includestorage other than primary storage located within computing environment114 that is directly accessible by processors located therein.Network-attached data stores may include secondary, tertiary orauxiliary storage, such as large hard drives, servers, virtual memory,among other types. Storage devices may include portable or non-portablestorage devices, optical storage devices, and various other mediumscapable of storing, containing data. A machine-readable storage mediumor computer-readable storage medium may include a non-transitory mediumin which data can be stored and that does not include carrier waves ortransitory electronic communications. Examples of a non-transitorymedium may include, for example, a magnetic disk or tape, opticalstorage media such as compact disk or digital versatile disk, flashmemory, memory or memory devices. A computer-program product may includecode or machine-executable instructions that may represent a procedure,a function, a subprogram, a program, a routine, a subroutine, a module,a software package, a class, or any combination of instructions, datastructures, or program statements. A code segment may be coupled toanother code segment or a hardware circuit by passing or receivinginformation, data, arguments, parameters, or memory contents.Information, arguments, parameters, data, etc. may be passed, forwarded,or transmitted via any suitable means including memory sharing, messagepassing, token passing, network transmission, among others. Furthermore,the data stores may hold a variety of different types of data. Forexample, network-attached data stores 110 may hold unstructured (e.g.,raw) data.

The unstructured data may be presented to the computing environment 114in different forms such as a flat file or a conglomerate of datarecords, and may have data values and accompanying time stamps. Thecomputing environment 114 may be used to analyze the unstructured datain a variety of ways to determine the best way to structure (e.g.,hierarchically) that data, such that the structured data is tailored toa type of further analysis that a user wishes to perform on the data.For example, after being processed, the unstructured time-stamped datamay be aggregated by time (e.g., into daily time period units) togenerate time series data or structured hierarchically according to oneor more dimensions (e.g., parameters, attributes, or variables). Forexample, data may be stored in a hierarchical data structure, such as arelational online analytical processing (ROLAP) or multidimensionalonline analytical processing (MOLAP) database, or may be stored inanother tabular form, such as in a flat-hierarchy form.

Data transmission network 100 may also include one or more server farms106. Computing environment 114 may route select communications or datato the server farms 106 or one or more servers within the server farms106. Server farms 106 can be configured to provide information in apredetermined manner. For example, server farms 106 may access data totransmit in response to a communication. Server farms 106 may beseparately housed from each other device within data transmissionnetwork 100, such as computing environment 114, or may be part of adevice or system.

Server farms 106 may host a variety of different types of dataprocessing as part of data transmission network 100. Server farms 106may receive a variety of different data from network devices, fromcomputing environment 114, from cloud network 116, or from othersources. The data may have been obtained or collected from one or morewebsites, sensors, as inputs from a control database, or may have beenreceived as inputs from an external system or device. Server farms 106may assist in processing the data by turning raw data into processeddata based on one or more rules implemented by the server farms. Forexample, sensor data may be analyzed to determine changes in anenvironment over time or in real-time.

Data transmission network 100 may also include one or more cloudnetworks 116. Cloud network 116 may include a cloud infrastructuresystem that provides cloud services. In certain examples, servicesprovided by the cloud network 116 may include a host of services thatare made available to users of the cloud infrastructure system ondemand. Cloud network 116 is shown in FIG. 1 as being connected tocomputing environment 114 (and therefore having computing environment114 as its client or user), but cloud network 116 may be connected to orutilized by any of the devices in FIG. 1. Services provided by the cloudnetwork 116 can dynamically scale to meet the needs of its users. Thecloud network 116 may include one or more computers, servers, orsystems. In some examples, the computers, servers, or systems that makeup the cloud network 116 are different from the user's own on-premisescomputers, servers, or systems. For example, the cloud network 116 mayhost an application, and a user may, via a communication network such asthe Internet, order and use the application on demand. In some examples,the cloud network 116 may host an application for managing bulk requestsand ad-hoc requests.

While each device, server, and system in FIG. 1 is shown as a singledevice, multiple devices may instead be used. For example, a set ofnetwork devices can be used to transmit various communications from asingle user, or remote server 140 may include a server stack. As anotherexample, data may be processed as part of computing environment 114.

Each communication within data transmission network 100 (e.g., betweenclient devices, between a device and connection management system 150,between server farms 106 and computing environment 114, or between aserver and a device) may occur over one or more networks 108. Networks108 may include one or more of a variety of different types of networks,including a wireless network, a wired network, or a combination of awired and wireless network. Examples of suitable networks include theInternet, a personal area network, a local area network (LAN), a widearea network (WAN), or a wireless local area network (WLAN). A wirelessnetwork may include a wireless interface or combination of wirelessinterfaces. As an example, a network in the one or more networks 108 mayinclude a short-range communication channel, such as a Bluetooth or aBluetooth Low Energy channel. A wired network may include a wiredinterface. The wired or wireless networks may be implemented usingrouters, access points, bridges, gateways, or the like, to connectdevices in the network 108. The networks 108 can be incorporatedentirely within or can include an intranet, an extranet, or acombination thereof. In one example, communications between two or moresystems or devices can be achieved by a secure communications protocol,such as secure sockets layer (SSL) or transport layer security (TLS). Inaddition, data or transactional details may be encrypted.

Some aspects may utilize the Internet of Things (IoT), where things(e.g., machines, devices, phones, and sensors) can be connected tonetworks and the data from these things can be collected and processedwithin the things or external to the things. For example, the IoT caninclude sensors in many different devices, and high value analytics canbe applied to identify hidden relationships and drive increasedefficiencies. This can apply to both big data analytics and real-time(e.g., ESP) analytics.

As noted, computing environment 114 may include a communications grid120 and a transmission network database system 118. Communications grid120 may be a grid-based computing system for processing large amounts ofdata. The transmission network database system 118 may be for managing,storing, and retrieving large amounts of data that are distributed toand stored in the one or more network-attached data stores 110 or otherdata stores that reside at different locations within the transmissionnetwork database system 118. The computing nodes in the communicationsgrid 120 and the transmission network database system 118 may share thesame processor hardware, such as processors that are located withincomputing environment 114.

In some examples, the computing environment 114, a network device 102,or both can implement one or more processes for handling bulk requestsand ad-hoc requests. For example, the computing environment 114, anetwork device 102, or both can implement one or more versions of theprocesses discussed with respect to any of the figures.

FIG. 2 is an example of devices that can communicate with each otherover an exchange system and via a network according to some aspects. Asnoted, each communication within data transmission network 100 may occurover one or more networks. System 200 includes a network device 204configured to communicate with a variety of types of client devices, forexample client devices 230, over a variety of types of communicationchannels.

As shown in FIG. 2, network device 204 can transmit a communication overa network (e.g., a cellular network via a base station 210). In someexamples, the communication can include times series data. Thecommunication can be routed to another network device, such as networkdevices 205-209, via base station 210. The communication can also berouted to computing environment 214 via base station 210. In someexamples, the network device 204 may collect data either from itssurrounding environment or from other network devices (such as networkdevices 205-209) and transmit that data to computing environment 214.

Although network devices 204-209 are shown in FIG. 2 as a mobile phone,laptop computer, tablet computer, temperature sensor, motion sensor, andaudio sensor respectively, the network devices may be or include sensorsthat are sensitive to detecting aspects of their environment. Forexample, the network devices may include sensors such as water sensors,power sensors, electrical current sensors, chemical sensors, opticalsensors, pressure sensors, geographic or position sensors (e.g., GPS),velocity sensors, acceleration sensors, flow rate sensors, among others.Examples of characteristics that may be sensed include force, torque,load, strain, position, temperature, air pressure, fluid flow, chemicalproperties, resistance, electromagnetic fields, radiation, irradiance,proximity, acoustics, moisture, distance, speed, vibrations,acceleration, electrical potential, and electrical current, amongothers. The sensors may be mounted to various components used as part ofa variety of different types of systems. The network devices may detectand record data related to the environment that it monitors, andtransmit that data to computing environment 214.

The network devices 204-209 may also perform processing on data itcollects before transmitting the data to the computing environment 214,or before deciding whether to transmit data to the computing environment214. For example, network devices 204-209 may determine whether datacollected meets certain rules, for example by comparing data or valuescalculated from the data and comparing that data to one or morethresholds. The network devices 204-209 may use this data or comparisonsto determine if the data is to be transmitted to the computingenvironment 214 for further use or processing. In some examples, thenetwork devices 204-209 can pre-process the data prior to transmittingthe data to the computing environment 214. For example, the networkdevices 204-209 can reformat the data before transmitting the data tothe computing environment 214 for further processing.

Computing environment 214 may include machines 220, 240. Althoughcomputing environment 214 is shown in FIG. 2 as having two machines 220,240, computing environment 214 may have only one machine or may havemore than two machines. The machines 220, 240 that make up computingenvironment 214 may include specialized computers, servers, or othermachines that are configured to individually or collectively processlarge amounts of data. The computing environment 214 may also includestorage devices that include one or more databases of structured data,such as data organized in one or more hierarchies, or unstructured data.The databases may communicate with the processing devices withincomputing environment 214 to distribute data to them. Since networkdevices may transmit data to computing environment 214, that data may bereceived by the computing environment 214 and subsequently stored withinthose storage devices. Data used by computing environment 214 may alsobe stored in data stores 235, which may also be a part of or connectedto computing environment 214.

Computing environment 214 can communicate with various devices via oneor more routers 225 or other inter-network or intra-network connectioncomponents. For example, computing environment 214 may communicate withclient devices 230 via one or more routers 225. Computing environment214 may collect, analyze or store data from or pertaining tocommunications, client device operations, client rules, oruser-associated actions stored at one or more data stores 235. Such datamay influence communication routing to the devices within computingenvironment 214, how data is stored or processed within computingenvironment 214, among other actions.

Notably, various other devices can further be used to influencecommunication routing or processing between devices within computingenvironment 214 and with devices outside of computing environment 214.For example, as shown in FIG. 2, computing environment 214 may include amachine 240 that is a web server. Computing environment 214 can retrievedata of interest, such as client information (e.g., product information,client rules, etc.), technical product details, news, blog posts,e-mails, forum posts, electronic documents, social media posts (e.g.,Twitter™ posts or Facebook™ posts), time series data, and so on.

In addition to computing environment 214 collecting data (e.g., asreceived from network devices, such as sensors, and client devices orother sources) to be processed as part of a big data analytics project,it may also receive data in real time as part of a streaming analyticsenvironment. As noted, data may be collected using a variety of sourcesas communicated via different kinds of networks or locally. Such datamay be received on a real-time streaming basis. For example, networkdevices 204-209 may receive data periodically and in real time from aweb server or other source. Devices within computing environment 214 mayalso perform pre-analysis on data it receives to determine if the datareceived should be processed as part of an ongoing project. For example,the computing environment 214 can perform a pre-analysis of the data.The pre-analysis can include determining whether the data is in acorrect format and, if not, reformatting the data into the correctformat.

FIG. 3 is a block diagram of a model of an example of a communicationsprotocol system according to some aspects. More specifically, FIG. 3identifies operation of a computing environment in an Open SystemsInteraction model that corresponds to various connection components. Themodel 300 shows, for example, how a computing environment, such ascomputing environment (or computing environment 214 in FIG. 2) maycommunicate with other devices in its network, and control howcommunications between the computing environment and other devices areexecuted and under what conditions.

The model 300 can include layers 302-314. The layers 302-314 arearranged in a stack. Each layer in the stack serves the layer one levelhigher than it (except for the application layer, which is the highestlayer), and is served by the layer one level below it (except for thephysical layer 302, which is the lowest layer). The physical layer 302is the lowest layer because it receives and transmits raw bites of data,and is the farthest layer from the user in a communications system. Onthe other hand, the application layer is the highest layer because itinteracts directly with a software application.

As noted, the model 300 includes a physical layer 302. Physical layer302 represents physical communication, and can define parameters of thatphysical communication. For example, such physical communication maycome in the form of electrical, optical, or electromagneticcommunications. Physical layer 302 also defines protocols that maycontrol communications within a data transmission network.

Link layer 304 defines links and mechanisms used to transmit (e.g.,move) data across a network. The link layer manages node-to-nodecommunications, such as within a grid-computing environment. Link layer304 can detect and correct errors (e.g., transmission errors in thephysical layer 302). Link layer 304 can also include a media accesscontrol (MAC) layer and logical link control (LLC) layer.

Network layer 306 can define the protocol for routing within a network.In other words, the network layer coordinates transferring data acrossnodes in a same network (e.g., such as a grid-computing environment).Network layer 306 can also define the processes used to structure localaddressing within the network.

Transport layer 308 can manage the transmission of data and the qualityof the transmission or receipt of that data. Transport layer 308 canprovide a protocol for transferring data, such as, for example, aTransmission Control Protocol (TCP). Transport layer 308 can assembleand disassemble data frames for transmission. The transport layer canalso detect transmission errors occurring in the layers below it.

Session layer 310 can establish, maintain, and manage communicationconnections between devices on a network. In other words, the sessionlayer controls the dialogues or nature of communications between networkdevices on the network. The session layer may also establishcheckpointing, adjournment, termination, and restart procedures.

Presentation layer 312 can provide translation for communicationsbetween the application and network layers. In other words, this layermay encrypt, decrypt or format data based on data types known to beaccepted by an application or network layer.

Application layer 314 interacts directly with software applications andend users, and manages communications between them. Application layer314 can identify destinations, local resource states or availability orcommunication content or formatting using the applications.

For example, a communication link can be established between two deviceson a network. One device can transmit an analog or digitalrepresentation of an electronic message that includes a data set to theother device. The other device can receive the analog or digitalrepresentation at the physical layer 302. The other device can transmitthe data associated with the electronic message through the remaininglayers 304-314. The application layer 314 can receive data associatedwith the electronic message. The application layer 314 can identify oneor more applications, such as an application for handling bulk requestsand ad-hoc requests, to which to transmit data associated with theelectronic message. The application layer 314 can transmit the data tothe identified application.

Intra-network connection components 322, 324 can operate in lowerlevels, such as physical layer 302 and link layer 304, respectively. Forexample, a hub can operate in the physical layer, a switch can operatein the physical layer, and a router can operate in the network layer.Inter-network connection components 326, 328 are shown to operate onhigher levels, such as layers 306-314. For example, routers can operatein the network layer and network devices can operate in the transport,session, presentation, and application layers.

A computing environment 330 can interact with or operate on, in variousexamples, one, more, all or any of the various layers. For example,computing environment 330 can interact with a hub (e.g., via the linklayer) to adjust which devices the hub communicates with. The physicallayer 302 may be served by the link layer 304, so it may implement suchdata from the link layer 304. For example, the computing environment 330may control which devices from which it can receive data. For example,if the computing environment 330 knows that a certain network device hasturned off, broken, or otherwise become unavailable or unreliable, thecomputing environment 330 may instruct the hub to prevent any data frombeing transmitted to the computing environment 330 from that networkdevice. Such a process may be beneficial to avoid receiving data that isinaccurate or that has been influenced by an uncontrolled environment.As another example, computing environment 330 can communicate with abridge, switch, router or gateway and influence which device within thesystem (e.g., system 200) the component selects as a destination. Insome examples, computing environment 330 can interact with variouslayers by exchanging communications with equipment operating on aparticular layer by routing or modifying existing communications. Inanother example, such as in a grid-computing environment, a node maydetermine how data within the environment should be routed (e.g., whichnode should receive certain data) based on certain parameters orinformation provided by other layers within the model.

The computing environment 330 may be a part of a communications gridenvironment, the communications of which may be implemented as shown inthe protocol of FIG. 3. For example, referring back to FIG. 2, one ormore of machines 220 and 240 may be part of a communicationsgrid-computing environment. A gridded computing environment may beemployed in a distributed system with non-interactive workloads wheredata resides in memory on the machines, or compute nodes. In such anenvironment, analytic code, instead of a database management system, cancontrol the processing performed by the nodes. Data is co-located bypre-distributing it to the grid nodes, and the analytic code on eachnode loads the local data into memory. Each node may be assigned aparticular task, such as a portion of a processing project, or toorganize or control other nodes within the grid. For example, each nodemay be assigned a portion of a processing task for handling bulkrequests and ad-hoc requests.

FIG. 4 is a hierarchical diagram of an example of a communications gridcomputing system 400 including a variety of control and worker nodesaccording to some aspects. Communications grid computing system 400includes three control nodes and one or more worker nodes.Communications grid computing system 400 includes control nodes 402,404, and 406. The control nodes are communicatively connected viacommunication paths 451, 453, and 455. The control nodes 402-406 maytransmit information (e.g., related to the communications grid ornotifications) to and receive information from each other. Althoughcommunications grid computing system 400 is shown in FIG. 4 as includingthree control nodes, the communications grid may include more or lessthan three control nodes.

Communications grid computing system 400 (which can be referred to as a“communications grid”) also includes one or more worker nodes. Shown inFIG. 4 are six worker nodes 410-420. Although FIG. 4 shows six workernodes, a communications grid can include more or less than six workernodes. The number of worker nodes included in a communications grid maybe dependent upon how large the project or data set is being processedby the communications grid, the capacity of each worker node, the timedesignated for the communications grid to complete the project, amongothers. Each worker node within the communications grid computing system400 may be connected (wired or wirelessly, and directly or indirectly)to control nodes 402-406. Each worker node may receive information fromthe control nodes (e.g., an instruction to perform work on a project)and may transmit information to the control nodes (e.g., a result fromwork performed on a project). Furthermore, worker nodes may communicatewith each other directly or indirectly. For example, worker nodes maytransmit data between each other related to a job being performed or anindividual task within a job being performed by that worker node. Insome examples, worker nodes may not be connected (communicatively orotherwise) to certain other worker nodes. For example, a worker node 410may only be able to communicate with a particular control node 402. Theworker node 410 may be unable to communicate with other worker nodes412-420 in the communications grid, even if the other worker nodes412-420 are controlled by the same control node 402.

A control node 402-406 may connect with an external device with whichthe control node 402-406 may communicate (e.g., a communications griduser, such as a server or computer, may connect to a controller of thegrid). For example, a server or computer may connect to control nodes402-406 and may transmit a project or job to the node, such as a projector job related to handling bulk requests and ad-hoc requests. Theproject may include the data set. The data set may be of any size. Oncethe control node 402-406 receives such a project including a large dataset, the control node may distribute the data set or projects related tothe data set to be performed by worker nodes. Alternatively, for aproject including a large data set, the data set may be receive orstored by a machine other than a control node 402-406 (e.g., a Hadoopdata node).

Control nodes 402-406 can maintain knowledge of the status of the nodesin the grid (e.g., grid status information), accept work requests fromclients, subdivide the work across worker nodes, and coordinate theworker nodes, among other responsibilities. Worker nodes 412-420 mayaccept work requests from a control node 402-406 and provide the controlnode with results of the work performed by the worker node. A grid maybe started from a single node (e.g., a machine, computer, server, etc.).This first node may be assigned or may start as the primary control node402 that will control any additional nodes that enter the grid.

When a project is submitted for execution (e.g., by a client or acontroller of the grid) it may be assigned to a set of nodes. After thenodes are assigned to a project, a data structure (e.g., a communicator)may be created. The communicator may be used by the project forinformation to be shared between the project code running on each node.A communication handle may be created on each node. A handle, forexample, is a reference to the communicator that is valid within asingle process on a single node, and the handle may be used whenrequesting communications between nodes.

A control node, such as control node 402, may be designated as theprimary control node. A server, computer or other external device mayconnect to the primary control node. Once the control node 402 receivesa project, the primary control node may distribute portions of theproject to its worker nodes for execution. For example, a project forhandling bulk requests and ad-hoc requests can be initiated oncommunications grid computing system 400. A primary control node cancontrol the work to be performed for the project in order to completethe project as requested or instructed. The primary control node maydistribute work to the worker nodes 412-420 based on various factors,such as which subsets or portions of projects may be completed mostefficiently and in the correct amount of time. For example, a workernode 412 may determine a bulk-parameter value using at least a portionof data that is already local (e.g., stored on) the worker node. Theprimary control node also coordinates and processes the results of thework performed by each worker node 412-420 after each worker node412-420 executes and completes its job. For example, the primary controlnode may receive a result from one or more worker nodes 412-420, and theprimary control node may organize (e.g., collect and assemble) theresults received and compile them to produce a complete result for theproject received from the end user.

Any remaining control nodes, such as control nodes 404, 406, may beassigned as backup control nodes for the project. In an example, backupcontrol nodes may not control any portion of the project. Instead,backup control nodes may serve as a backup for the primary control nodeand take over as primary control node if the primary control node wereto fail. If a communications grid were to include only a single controlnode 402, and the control node 402 were to fail (e.g., the control nodeis shut off or breaks) then the communications grid as a whole may failand any project or job being run on the communications grid may fail andmay not complete. While the project may be run again, such a failure maycause a delay (severe delay in some cases, such as overnight delay) incompletion of the project. Therefore, a grid with multiple control nodes402-406, including a backup control node, may be beneficial.

In some examples, the primary control node may open a pair of listeningsockets to add another node or machine to the grid. A socket may be usedto accept work requests from clients, and the second socket may be usedto accept connections from other grid nodes. The primary control nodemay be provided with a list of other nodes (e.g., other machines,computers, servers, etc.) that can participate in the grid, and the rolethat each node can fill in the grid. Upon startup of the primary controlnode (e.g., the first node on the grid), the primary control node mayuse a network protocol to start the server process on every other nodein the grid. Command line parameters, for example, may inform each nodeof one or more pieces of information, such as: the role that the nodewill have in the grid, the host name of the primary control node, theport number on which the primary control node is accepting connectionsfrom peer nodes, among others. The information may also be provided in aconfiguration file, transmitted over a secure shell tunnel, recoveredfrom a configuration server, among others. While the other machines inthe grid may not initially know about the configuration of the grid,that information may also be sent to each other node by the primarycontrol node. Updates of the grid information may also be subsequentlysent to those nodes.

For any control node other than the primary control node added to thegrid, the control node may open three sockets. The first socket mayaccept work requests from clients, the second socket may acceptconnections from other grid members, and the third socket may connect(e.g., permanently) to the primary control node. When a control node(e.g., primary control node) receives a connection from another controlnode, it first checks to see if the peer node is in the list ofconfigured nodes in the grid. If it is not on the list, the control nodemay clear the connection. If it is on the list, it may then attempt toauthenticate the connection. If authentication is successful, theauthenticating node may transmit information to its peer, such as theport number on which a node is listening for connections, the host nameof the node, information about how to authenticate the node, among otherinformation. When a node, such as the new control node, receivesinformation about another active node, it can check to see if it alreadyhas a connection to that other node. If it does not have a connection tothat node, it may then establish a connection to that control node.

Any worker node added to the grid may establish a connection to theprimary control node and any other control nodes on the grid. Afterestablishing the connection, it may authenticate itself to the grid(e.g., any control nodes, including both primary and backup, or a serveror user controlling the grid). After successful authentication, theworker node may accept configuration information from the control node.

When a node joins a communications grid (e.g., when the node is poweredon or connected to an existing node on the grid or both), the node isassigned (e.g., by an operating system of the grid) a universally uniqueidentifier (UUID). This unique identifier may help other nodes andexternal entities (devices, users, etc.) to identify the node anddistinguish it from other nodes. When a node is connected to the grid,the node may share its unique identifier with the other nodes in thegrid. Since each node may share its unique identifier, each node mayknow the unique identifier of every other node on the grid. Uniqueidentifiers may also designate a hierarchy of each of the nodes (e.g.,backup control nodes) within the grid. For example, the uniqueidentifiers of each of the backup control nodes may be stored in a listof backup control nodes to indicate an order in which the backup controlnodes will take over for a failed primary control node to become a newprimary control node. But, a hierarchy of nodes may also be determinedusing methods other than using the unique identifiers of the nodes. Forexample, the hierarchy may be predetermined, or may be assigned based onother predetermined factors.

The grid may add new machines at any time (e.g., initiated from anycontrol node). Upon adding a new node to the grid, the control node mayfirst add the new node to its table of grid nodes. The control node mayalso then notify every other control node about the new node. The nodesreceiving the notification may acknowledge that they have updated theirconfiguration information.

Primary control node 402 may, for example, transmit one or morecommunications to backup control nodes 404, 406 (and, for example, toother control or worker nodes 412-420 within the communications grid).Such communications may be sent periodically, at fixed time intervals,between known fixed stages of the project's execution, among otherprotocols. The communications transmitted by primary control node 402may be of varied types and may include a variety of types ofinformation. For example, primary control node 402 may transmitsnapshots (e.g., status information) of the communications grid so thatbackup control node 404 always has a recent snapshot of thecommunications grid. The snapshot or grid status may include, forexample, the structure of the grid (including, for example, the workernodes 410-420 in the communications grid, unique identifiers of theworker nodes 410-420, or their relationships with the primary controlnode 402) and the status of a project (including, for example, thestatus of each worker node's portion of the project). The snapshot mayalso include analysis or results received from worker nodes 410-420 inthe communications grid. The backup control nodes 404, 406 may receiveand store the backup data received from the primary control node 402.The backup control nodes 404, 406 may transmit a request for such asnapshot (or other information) from the primary control node 402, orthe primary control node 402 may send such information periodically tothe backup control nodes 404, 406.

As noted, the backup data may allow a backup control node 404, 406 totake over as primary control node if the primary control node 402 failswithout requiring the communications grid to start the project over fromscratch. If the primary control node 402 fails, the backup control node404, 406 that will take over as primary control node may retrieve themost recent version of the snapshot received from the primary controlnode 402 and use the snapshot to continue the project from the stage ofthe project indicated by the backup data. This may prevent failure ofthe project as a whole.

A backup control node 404, 406 may use various methods to determine thatthe primary control node 402 has failed. In one example of such amethod, the primary control node 402 may transmit (e.g., periodically) acommunication to the backup control node 404, 406 that indicates thatthe primary control node 402 is working and has not failed, such as aheartbeat communication. The backup control node 404, 406 may determinethat the primary control node 402 has failed if the backup control nodehas not received a heartbeat communication for a certain predeterminedperiod of time. Alternatively, a backup control node 404, 406 may alsoreceive a communication from the primary control node 402 itself (beforeit failed) or from a worker node 410-420 that the primary control node402 has failed, for example because the primary control node 402 hasfailed to communicate with the worker node 410-420.

Different methods may be performed to determine which backup controlnode of a set of backup control nodes (e.g., backup control nodes 404,406) can take over for failed primary control node 402 and become thenew primary control node. For example, the new primary control node maybe chosen based on a ranking or “hierarchy” of backup control nodesbased on their unique identifiers. In an alternative example, a backupcontrol node may be assigned to be the new primary control node byanother device in the communications grid or from an external device(e.g., a system infrastructure or an end user, such as a server orcomputer, controlling the communications grid). In another alternativeexample, the backup control node that takes over as the new primarycontrol node may be designated based on bandwidth or other statisticsabout the communications grid.

A worker node within the communications grid may also fail. If a workernode fails, work being performed by the failed worker node may beredistributed amongst the operational worker nodes. In an alternativeexample, the primary control node may transmit a communication to eachof the operable worker nodes still on the communications grid that eachof the worker nodes should purposefully fail also. After each of theworker nodes fail, they may each retrieve their most recent savedcheckpoint of their status and re-start the project from that checkpointto minimize lost progress on the project being executed. In someexamples, a communications grid computing system 400 can be used todetermine how to handle bulk requests and ad-hoc requests, such as whichparameter values to assign to bulk requests and ad-hoc requests.

FIG. 5 is a flow chart of an example of a process for adjusting acommunications grid or a work project in a communications grid after afailure of a node according to some aspects. The process may include,for example, receiving grid status information including a projectstatus of a portion of a project being executed by a node in thecommunications grid, as described in operation 502. For example, acontrol node (e.g., a backup control node connected to a primary controlnode and a worker node on a communications grid) may receive grid statusinformation, where the grid status information includes a project statusof the primary control node or a project status of the worker node. Theproject status of the primary control node and the project status of theworker node may include a status of one or more portions of a projectbeing executed by the primary and worker nodes in the communicationsgrid. The process may also include storing the grid status information,as described in operation 504. For example, a control node (e.g., abackup control node) may store the received grid status informationlocally within the control node. Alternatively, the grid statusinformation may be sent to another device for storage where the controlnode may have access to the information.

The process may also include receiving a failure communicationcorresponding to a node in the communications grid in operation 506. Forexample, a node may receive a failure communication including anindication that the primary control node has failed, prompting a backupcontrol node to take over for the primary control node. In analternative embodiment, a node may receive a failure that a worker nodehas failed, prompting a control node to reassign the work beingperformed by the worker node. The process may also include reassigning anode or a portion of the project being executed by the failed node, asdescribed in operation 508. For example, a control node may designatethe backup control node as a new primary control node based on thefailure communication upon receiving the failure communication. If thefailed node is a worker node, a control node may identify a projectstatus of the failed worker node using the snapshot of thecommunications grid, where the project status of the failed worker nodeincludes a status of a portion of the project being executed by thefailed worker node at the failure time.

The process may also include receiving updated grid status informationbased on the reassignment, as described in operation 510, andtransmitting a set of instructions based on the updated grid statusinformation to one or more nodes in the communications grid, asdescribed in operation 512. The updated grid status information mayinclude an updated project status of the primary control node or anupdated project status of the worker node. The updated information maybe transmitted to the other nodes in the grid to update their stalestored information.

FIG. 6 is a block diagram of a portion of a communications gridcomputing system 600 including a control node and a worker nodeaccording to some aspects. Communications grid 600 computing systemincludes one control node (control node 602) and one worker node (workernode 610) for purposes of illustration, but may include more workerand/or control nodes. The control node 602 is communicatively connectedto worker node 610 via communication path 650. Therefore, control node602 may transmit information (e.g., related to the communications gridor notifications), to and receive information from worker node 610 viacommunication path 650.

Similar to in FIG. 4, communications grid computing system (or just“communications grid”) 600 includes data processing nodes (control node602 and worker node 610). Nodes 602 and 610 comprise multi-core dataprocessors. Each node 602 and 610 includes a grid-enabled softwarecomponent (GESC) 620 that executes on the data processor associated withthat node and interfaces with buffer memory 622 also associated withthat node. Each node 602 and 610 includes database management software(DBMS) 628 that executes on a database server (not shown) at controlnode 602 and on a database server (not shown) at worker node 610.

Each node also includes a data store 624. Data stores 624, similar tonetwork-attached data stores 110 in FIG. 1 and data stores 235 in FIG.2, are used to store data to be processed by the nodes in the computingenvironment. Data stores 624 may also store any intermediate or finaldata generated by the computing system after being processed, forexample in non-volatile memory. However in certain examples, theconfiguration of the grid computing environment allows its operations tobe performed such that intermediate and final data results can be storedsolely in volatile memory (e.g., RAM), without a requirement thatintermediate or final data results be stored to non-volatile types ofmemory. Storing such data in volatile memory may be useful in certainsituations, such as when the grid receives queries (e.g., ad hoc) from aclient and when responses, which are generated by processing largeamounts of data, need to be generated quickly or on-the-fly. In such asituation, the grid may be configured to retain the data within memoryso that responses can be generated at different levels of detail and sothat a client may interactively query against this information.

Each node also includes a user-defined function (UDF) 626. The UDFprovides a mechanism for the DMBS 628 to transfer data to or receivedata from the database stored in the data stores 624 that are managed bythe DBMS. For example, UDF 626 can be invoked by the DBMS to providedata to the GESC for processing. The UDF 626 may establish a socketconnection (not shown) with the GESC to transfer the data.Alternatively, the UDF 626 can transfer data to the GESC by writing datato shared memory accessible by both the UDF and the GESC.

The GESC 620 at the nodes 602 and 610 may be connected via a network,such as network 108 shown in FIG. 1. Therefore, nodes 602 and 610 cancommunicate with each other via the network using a predeterminedcommunication protocol such as, for example, the Message PassingInterface (MPI). Each GESC 620 can engage in point-to-pointcommunication with the GESC at another node or in collectivecommunication with multiple GESCs via the network. The GESC 620 at eachnode may contain identical (or nearly identical) software instructions.Each node may be capable of operating as either a control node or aworker node. The GESC at the control node 602 can communicate, over acommunication path 652, with a client device 630. More specifically,control node 602 may communicate with client application 632 hosted bythe client device 630 to receive queries and to respond to those queriesafter processing large amounts of data.

DMBS 628 may control the creation, maintenance, and use of database ordata structure (not shown) within nodes 602 or 610. The database mayorganize data stored in data stores 624. The DMBS 628 at control node602 may accept requests for data and transfer the appropriate data forthe request. With such a process, collections of data may be distributedacross multiple physical locations. In this example, each node 602 and610 stores a portion of the total data managed by the management systemin its associated data store 624.

Furthermore, the DBMS may be responsible for protecting against dataloss using replication techniques. Replication includes providing abackup copy of data stored on one node on one or more other nodes.Therefore, if one node fails, the data from the failed node can berecovered from a replicated copy residing at another node. However, asdescribed herein with respect to FIG. 4, data or status information foreach node in the communications grid may also be shared with each nodeon the grid.

FIG. 7 is a flow chart of an example of a process for executing a dataanalysis or a processing project according to some aspects. As describedwith respect to FIG. 6, the GESC at the control node may transmit datawith a client device (e.g., client device 630) to receive queries forexecuting a project and to respond to those queries after large amountsof data have been processed. The query may be transmitted to the controlnode, where the query may include a request for executing a project, asdescribed in operation 702. The query can contain instructions on thetype of data analysis to be performed in the project and whether theproject should be executed using the grid-based computing environment,as shown in operation 704.

To initiate the project, the control node may determine if the queryrequests use of the grid-based computing environment to execute theproject. If the determination is no, then the control node initiatesexecution of the project in a solo environment (e.g., at the controlnode), as described in operation 710. If the determination is yes, thecontrol node may initiate execution of the project in the grid-basedcomputing environment, as described in operation 706. In such asituation, the request may include a requested configuration of thegrid. For example, the request may include a number of control nodes anda number of worker nodes to be used in the grid when executing theproject. After the project has been completed, the control node maytransmit results of the analysis yielded by the grid, as described inoperation 708. Whether the project is executed in a solo or grid-basedenvironment, the control node provides the results of the project.

As noted with respect to FIG. 2, the computing environments describedherein may collect data (e.g., as received from network devices, such assensors, such as network devices 204-209 in FIG. 2, and client devicesor other sources) to be processed as part of a data analytics project,and data may be received in real time as part of a streaming analyticsenvironment (e.g., ESP). Data may be collected using a variety ofsources as communicated via different kinds of networks or locally, suchas on a real-time streaming basis. For example, network devices mayreceive data periodically from network device sensors as the sensorscontinuously sense, monitor and track changes in their environments.More specifically, an increasing number of distributed applicationsdevelop or produce continuously flowing data from distributed sources byapplying queries to the data before distributing the data togeographically distributed recipients. An event stream processing engine(ESPE) may continuously apply the queries to the data as it is receivedand determines which entities should receive the data. Client or otherdevices may also subscribe to the ESPE or other devices processing ESPdata so that they can receive data after processing, based on forexample the entities determined by the processing engine. For example,client devices 230 in FIG. 2 may subscribe to the ESPE in computingenvironment 214. In another example, event subscription devices 1024a-c, described further with respect to FIG. 10, may also subscribe tothe ESPE. The ESPE may determine or define how input data or eventstreams from network devices or other publishers (e.g., network devices204-209 in FIG. 2) are transformed into meaningful output data to beconsumed by subscribers, such as for example client devices 230 in FIG.2.

FIG. 8 is a block diagram including components of an Event StreamProcessing Engine (ESPE) according to some aspects. ESPE 800 may includeone or more projects 802. A project may be described as a second-levelcontainer in an engine model managed by ESPE 800 where a thread poolsize for the project may be defined by a user. Each project of the oneor more projects 802 may include one or more continuous queries 804 thatcontain data flows, which are data transformations of incoming eventstreams. The one or more continuous queries 804 may include one or moresource windows 806 and one or more derived windows 808.

The ESPE may receive streaming data over a period of time related tocertain events, such as events or other data sensed by one or morenetwork devices. The ESPE may perform operations associated withprocessing data created by the one or more devices. For example, theESPE may receive data from the one or more network devices 204-209 shownin FIG. 2. As noted, the network devices may include sensors that sensedifferent aspects of their environments, and may collect data over timebased on those sensed observations. For example, the ESPE may beimplemented within one or more of machines 220 and 240 shown in FIG. 2.The ESPE may be implemented within such a machine by an ESP application.An ESP application may embed an ESPE with its own dedicated thread poolor pools into its application space where the main application threadcan do application-specific work and the ESPE processes event streams atleast by creating an instance of a model into processing objects.

The engine container is the top-level container in a model that managesthe resources of the one or more projects 802. In an illustrativeexample, there may be only one ESPE 800 for each instance of the ESPapplication, and ESPE 800 may have a unique engine name. Additionally,the one or more projects 802 may each have unique project names, andeach query may have a unique continuous query name and begin with auniquely named source window of the one or more source windows 806. ESPE800 may or may not be persistent.

Continuous query modeling involves defining directed graphs of windowsfor event stream manipulation and transformation. A window in thecontext of event stream manipulation and transformation is a processingnode in an event stream processing model. A window in a continuous querycan perform aggregations, computations, pattern-matching, and otheroperations on data flowing through the window. A continuous query may bedescribed as a directed graph of source, relational, pattern matching,and procedural windows. The one or more source windows 806 and the oneor more derived windows 808 represent continuously executing queriesthat generate updates to a query result set as new event blocks streamthrough ESPE 800. A directed graph, for example, is a set of nodesconnected by edges, where the edges have a direction associated withthem.

An event object may be described as a packet of data accessible as acollection of fields, with at least one of the fields defined as a keyor unique identifier (ID). The event object may be created using avariety of formats including binary, alphanumeric, XML, etc. Each eventobject may include one or more fields designated as a primary identifier(ID) for the event so ESPE 800 can support operation codes (opcodes) forevents including insert, update, upsert, and delete. Upsert opcodesupdate the event if the key field already exists; otherwise, the eventis inserted. For illustration, an event object may be a packed binaryrepresentation of a set of field values and include both metadata andfield data associated with an event. The metadata may include an opcodeindicating if the event represents an insert, update, delete, or upsert,a set of flags indicating if the event is a normal, partial-update, or aretention generated event from retention policy management, and a set ofmicrosecond timestamps that can be used for latency measurements.

An event block object may be described as a grouping or package of eventobjects. An event stream may be described as a flow of event blockobjects. A continuous query of the one or more continuous queries 804transforms a source event stream made up of streaming event blockobjects published into ESPE 800 into one or more output event streamsusing the one or more source windows 806 and the one or more derivedwindows 808. A continuous query can also be thought of as data flowmodeling.

The one or more source windows 806 are at the top of the directed graphand have no windows feeding into them. Event streams are published intothe one or more source windows 806, and from there, the event streamsmay be directed to the next set of connected windows as defined by thedirected graph. The one or more derived windows 808 are all instantiatedwindows that are not source windows and that have other windowsstreaming events into them. The one or more derived windows 808 mayperform computations or transformations on the incoming event streams.The one or more derived windows 808 transform event streams based on thewindow type (that is operators such as join, filter, compute, aggregate,copy, pattern match, procedural, union, etc.) and window settings. Asevent streams are published into ESPE 800, they are continuouslyqueried, and the resulting sets of derived windows in these queries arecontinuously updated.

FIG. 9 is a flow chart of an example of a process including operationsperformed by an event stream processing engine according to someaspects. As noted, the ESPE 800 (or an associated ESP application)defines how input event streams are transformed into meaningful outputevent streams. More specifically, the ESP application may define howinput event streams from publishers (e.g., network devices providingsensed data) are transformed into meaningful output event streamsconsumed by subscribers (e.g., a data analytics project being executedby a machine or set of machines).

Within the application, a user may interact with one or more userinterface windows presented to the user in a display under control ofthe ESPE independently or through a browser application in an orderselectable by the user. For example, a user may execute an ESPapplication, which causes presentation of a first user interface window,which may include a plurality of menus and selectors such as drop downmenus, buttons, text boxes, hyperlinks, etc. associated with the ESPapplication as understood by a person of skill in the art. Variousoperations may be performed in parallel, for example, using a pluralityof threads.

At operation 900, an ESP application may define and start an ESPE,thereby instantiating an ESPE at a device, such as machine 220 and/or240. In an operation 902, the engine container is created. Forillustration, ESPE 800 may be instantiated using a function call thatspecifies the engine container as a manager for the model.

In an operation 904, the one or more continuous queries 804 areinstantiated by ESPE 800 as a model. The one or more continuous queries804 may be instantiated with a dedicated thread pool or pools thatgenerate updates as new events stream through ESPE 800. Forillustration, the one or more continuous queries 804 may be created tomodel business processing logic within ESPE 800, to predict eventswithin ESPE 800, to model a physical system within ESPE 800, to predictthe physical system state within ESPE 800, etc. For example, as noted,ESPE 800 may be used to support sensor data monitoring and management(e.g., sensing may include force, torque, load, strain, position,temperature, air pressure, fluid flow, chemical properties, resistance,electromagnetic fields, radiation, irradiance, proximity, acoustics,moisture, distance, speed, vibrations, acceleration, electricalpotential, or electrical current, etc.).

ESPE 800 may analyze and process events in motion or “event streams.”Instead of storing data and running queries against the stored data,ESPE 800 may store queries and stream data through them to allowcontinuous analysis of data as it is received. The one or more sourcewindows 806 and the one or more derived windows 808 may be created basedon the relational, pattern matching, and procedural algorithms thattransform the input event streams into the output event streams tomodel, simulate, score, test, predict, etc. based on the continuousquery model defined and application to the streamed data.

In an operation 906, a publish/subscribe (pub/sub) capability isinitialized for ESPE 800. In an illustrative embodiment, a pub/subcapability is initialized for each project of the one or more projects802. To initialize and enable pub/sub capability for ESPE 800, a portnumber may be provided. Pub/sub clients can use a host name of an ESPdevice running the ESPE and the port number to establish pub/subconnections to ESPE 800.

FIG. 10 is a block diagram of an ESP system 1000 interfacing betweenpublishing device 1022 and event subscription devices 1024 a-c accordingto some aspects. ESP system 1000 may include ESP subsystem 1001,publishing device 1022, an event subscription device A 1024 a, an eventsubscription device B 1024 b, and an event subscription device C 1024 c.Input event streams are output to ESP subsystem 1001 by publishingdevice 1022. In alternative embodiments, the input event streams may becreated by a plurality of publishing devices. The plurality ofpublishing devices further may publish event streams to other ESPdevices. The one or more continuous queries instantiated by ESPE 800 mayanalyze and process the input event streams to form output event streamsoutput to event subscription device A 1024 a, event subscription deviceB 1024 b, and event subscription device C 1024 c. ESP system 1000 mayinclude a greater or a fewer number of event subscription devices ofevent subscription devices.

Publish-subscribe is a message-oriented interaction paradigm based onindirect addressing. Processed data recipients specify their interest inreceiving information from ESPE 800 by subscribing to specific classesof events, while information sources publish events to ESPE 800 withoutdirectly addressing the receiving parties. ESPE 800 coordinates theinteractions and processes the data. In some cases, the data sourcereceives confirmation that the published information has been receivedby a data recipient.

A publish/subscribe API may be described as a library that enables anevent publisher, such as publishing device 1022, to publish eventstreams into ESPE 800 or an event subscriber, such as event subscriptiondevice A 1024 a, event subscription device B 1024 b, and eventsubscription device C 1024 c, to subscribe to event streams from ESPE800. For illustration, one or more publish/subscribe APIs may bedefined. Using the publish/subscribe API, an event publishingapplication may publish event streams into a running event streamprocessor project source window of ESPE 800, and the event subscriptionapplication may subscribe to an event stream processor project sourcewindow of ESPE 800.

The publish/subscribe API provides cross-platform connectivity andendianness compatibility between ESP application and other networkedapplications, such as event publishing applications instantiated atpublishing device 1022, and event subscription applications instantiatedat one or more of event subscription device A 1024 a, event subscriptiondevice B 1024 b, and event subscription device C 1024 c.

Referring back to FIG. 9, operation 906 initializes thepublish/subscribe capability of ESPE 800. In an operation 908, the oneor more projects 802 are started. The one or more started projects mayrun in the background on an ESP device. In an operation 910, an eventblock object is received from one or more computing device of thepublishing device 1022.

ESP subsystem 1001 may include a publishing client 1002, ESPE 800, asubscribing client A 1004, a subscribing client B 1006, and asubscribing client C 1008. Publishing client 1002 may be started by anevent publishing application executing at publishing device 1022 usingthe publish/subscribe API. Subscribing client A 1004 may be started byan event subscription application A, executing at event subscriptiondevice A 1024 a using the publish/subscribe API. Subscribing client B1006 may be started by an event subscription application B executing atevent subscription device B 1024 b using the publish/subscribe API.Subscribing client C 1008 may be started by an event subscriptionapplication C executing at event subscription device C 1024 c using thepublish/subscribe API.

An event block object containing one or more event objects is injectedinto a source window of the one or more source windows 806 from aninstance of an event publishing application on publishing device 1022.The event block object may be generated, for example, by the eventpublishing application and may be received by publishing client 1002. Aunique ID may be maintained as the event block object is passed betweenthe one or more source windows 806 and/or the one or more derivedwindows 808 of ESPE 800, and to subscribing client A 1004, subscribingclient B 1006, and subscribing client C 1008 and to event subscriptiondevice A 1024 a, event subscription device B 1024 b, and eventsubscription device C 1024 c. Publishing client 1002 may furthergenerate and include a unique embedded transaction ID in the event blockobject as the event block object is processed by a continuous query, aswell as the unique ID that publishing device 1022 assigned to the eventblock object.

In an operation 912, the event block object is processed through the oneor more continuous queries 804. In an operation 914, the processed eventblock object is output to one or more computing devices of the eventsubscription devices 1024 a-c. For example, subscribing client A 1004,subscribing client B 1006, and subscribing client C 1008 may send thereceived event block object to event subscription device A 1024 a, eventsubscription device B 1024 b, and event subscription device C 1024 c,respectively.

ESPE 800 maintains the event block containership aspect of the receivedevent blocks from when the event block is published into a source windowand works its way through the directed graph defined by the one or morecontinuous queries 804 with the various event translations before beingoutput to subscribers. Subscribers can correlate a group of subscribedevents back to a group of published events by comparing the unique ID ofthe event block object that a publisher, such as publishing device 1022,attached to the event block object with the event block ID received bythe subscriber.

In an operation 916, a determination is made concerning whether or notprocessing is stopped. If processing is not stopped, processingcontinues in operation 910 to continue receiving the one or more eventstreams containing event block objects from the, for example, one ormore network devices. If processing is stopped, processing continues inan operation 918. In operation 918, the started projects are stopped. Inoperation 920, the ESPE is shutdown.

As noted, in some examples, big data is processed for an analyticsproject after the data is received and stored. In other examples,distributed applications process continuously flowing data in real-timefrom distributed sources by applying queries to the data beforedistributing the data to geographically distributed recipients. Asnoted, an event stream processing engine (ESPE) may continuously applythe queries to the data as it is received and determines which entitiesreceive the processed data. This allows for large amounts of data beingreceived and/or collected in a variety of environments to be processedand distributed in real time. For example, as shown with respect to FIG.2, data may be collected from network devices that may include deviceswithin the internet of things, such as devices within a home automationnetwork. However, such data may be collected from a variety of differentresources in a variety of different environments. In any such situation,embodiments of the present technology allow for real-time processing ofsuch data.

Aspects of the present disclosure provide technical solutions totechnical problems, such as computing problems that arise when an ESPdevice fails which results in a complete service interruption andpotentially significant data loss. The data loss can be catastrophicwhen the streamed data is supporting mission critical operations, suchas those in support of an ongoing manufacturing or drilling operation.An example of an ESP system achieves a rapid and seamless failover ofESPE running at the plurality of ESP devices without serviceinterruption or data loss, thus significantly improving the reliabilityof an operational system that relies on the live or real-time processingof the data streams. The event publishing systems, the event subscribingsystems, and each ESPE not executing at a failed ESP device are notaware of or effected by the failed ESP device. The ESP system mayinclude thousands of event publishing systems and event subscribingsystems. The ESP system keeps the failover logic and awareness withinthe boundaries of out-messaging network connector and out-messagingnetwork device.

In one example embodiment, a system is provided to support a failoverwhen event stream processing (ESP) event blocks. The system includes,but is not limited to, an out-messaging network device and a computingdevice. The computing device includes, but is not limited to, one ormore processors and one or more computer-readable mediums operablycoupled to the one or more processor. The processor is configured toexecute an ESP engine (ESPE). The computer-readable medium hasinstructions stored thereon that, when executed by the processor, causethe computing device to support the failover. An event block object isreceived from the ESPE that includes a unique identifier. A first statusof the computing device as active or standby is determined. When thefirst status is active, a second status of the computing device as newlyactive or not newly active is determined. Newly active is determinedwhen the computing device is switched from a standby status to an activestatus. When the second status is newly active, a last published eventblock object identifier that uniquely identifies a last published eventblock object is determined. A next event block object is selected from anon-transitory computer-readable medium accessible by the computingdevice. The next event block object has an event block object identifierthat is greater than the determined last published event block objectidentifier. The selected next event block object is published to anout-messaging network device. When the second status of the computingdevice is not newly active, the received event block object is publishedto the out-messaging network device. When the first status of thecomputing device is standby, the received event block object is storedin the non-transitory computer-readable medium.

FIG. 11 is a flow chart of an example of a process for generating andusing a machine-learning model according to some aspects. Machinelearning is a branch of artificial intelligence that relates tomathematical models that can learn from, categorize, and makepredictions about data. Such mathematical models, which can be referredto as machine-learning models, can classify input data among two or moreclasses; cluster input data among two or more groups; predict a resultbased on input data; identify patterns or trends in input data; identifya distribution of input data in a space; or any combination of these.Examples of machine-learning models can include (i) neural networks;(ii) decision trees, such as classification trees and regression trees;(iii) classifiers, such as naïve bias classifiers, logistic regressionclassifiers, ridge regression classifiers, random forest classifiers,least absolute shrinkage and selector (LASSO) classifiers, and supportvector machines; (iv) clusterers, such as k-means clusterers, mean-shiftclusterers, and spectral clusterers; (v) factorizers, such asfactorization machines, principal component analyzers and kernelprincipal component analyzers; and (vi) ensembles or other combinationsof machine-learning models. In some examples, neural networks caninclude deep neural networks, feed-forward neural networks, recurrentneural networks, convolutional neural networks, radial basis function(RBF) neural networks, echo state neural networks, long short-termmemory neural networks, bi-directional recurrent neural networks, gatedneural networks, hierarchical recurrent neural networks, stochasticneural networks, modular neural networks, spiking neural networks,dynamic neural networks, cascading neural networks, neuro-fuzzy neuralnetworks, or any combination of these.

Different machine-learning models may be used interchangeably to performa task. Examples of tasks that can be performed at least partially usingmachine-learning models include various types of scoring;bioinformatics; cheminformatics; software engineering; fraud detection;customer segmentation; generating online recommendations; adaptivewebsites; determining customer lifetime value; search engines; placingadvertisements in real time or near real time; classifying DNAsequences; affective computing; performing natural language processingand understanding; object recognition and computer vision; roboticlocomotion; playing games; optimization and metaheuristics; detectingnetwork intrusions; medical diagnosis and monitoring; or predicting whenan asset, such as a machine, will need maintenance.

Any number and combination of tools can be used to createmachine-learning models. Examples of tools for creating and usingmachine-learning models can include SAS Enterprise Miner (e.g., with theSAS Text Miner add-on), SAS Rapid Predictive Modeler, SAS Model Manager,SAS Cloud Analytic Services (CAS), and SAS Viya (e.g., including VisualText Analytics and Visual Analytics), all of which are by SAS InstituteInc.® of Cary, N.C.

Machine-learning models can be constructed through an at least partiallyautomated (e.g., with little or no human involvement) process calledtraining. During training, input data can be iteratively supplied to amachine-learning model to enable the machine-learning model to identifypatterns related to the input data or to identify relationships betweenthe input data and output data. With training, the machine-learningmodel can be transformed from an untrained state to a trained state.Input data can be split into one or more training sets and one or morevalidation sets, and the training process may be repeated multipletimes. The splitting may follow a k-fold cross-validation rule, aleave-one-out-rule, a leave-p-out rule, or a holdout rule. An overviewof training and using a machine-learning model is described below withrespect to the flow chart of FIG. 11.

In block 1104, training data is received. In some examples, the trainingdata is received from a remote database or a local database, constructedfrom various subsets of data, or input by a user. The training data canbe used in its raw form for training a machine-learning model orpre-processed into another form, which can then be used for training themachine-learning model. For example, the raw form of the training datacan be smoothed, truncated, aggregated, clustered, or otherwisemanipulated into another form, which can then be used for training themachine-learning model.

In block 1106, a machine-learning model is trained using the trainingdata. The machine-learning model can be trained in a supervised,unsupervised, or semi-supervised manner. In supervised training, eachinput in the training data is correlated to a desired output. Thisdesired output may be a scalar, a vector, or a different type of datastructure such as text or an image. This may enable the machine-learningmodel to learn a mapping between the inputs and desired outputs. Inunsupervised training, the training data includes inputs, but notdesired outputs, so that the machine-learning model has to findstructure in the inputs on its own. In semi-supervised training, onlysome of the inputs in the training data are correlated to desiredoutputs.

In block 1108, the machine-learning model is evaluated. An evaluationdataset can be obtained, for example, via user input or from a database.The evaluation dataset can include inputs correlated to desired outputs.The inputs can be provided to the machine-learning model and the outputsfrom the machine-learning model can be compared to the desired outputs.If the outputs from the machine-learning model closely correspond withthe desired outputs, the machine-learning model may have a high degreeof accuracy. For example, if 90% or more of the outputs from themachine-learning model are the same as the desired outputs in theevaluation dataset, the machine-learning model may have a high degree ofaccuracy. Otherwise, the machine-learning model may have a low degree ofaccuracy. The 90% number is an example only. A realistic and desirableaccuracy percentage is dependent on the problem and the data.

In some examples, if the machine-learning model has an inadequate degreeof accuracy for a particular task, the process can return to block 1106,where the machine-learning model can be further trained using additionaltraining data or otherwise modified to improve accuracy. If themachine-learning model has an adequate degree of accuracy for theparticular task, the process can continue to block 1110.

In block 1110, new data is received. In some examples, the new data isreceived from a remote database or a local database, constructed fromvarious subsets of data, or input by a user. The new data may be unknownto the machine-learning model. For example, the machine-learning modelmay not have previously processed or analyzed the new data.

In block 1112, the trained machine-learning model is used to analyze thenew data and provide a result. For example, the new data can be providedas input to the trained machine-learning model. The trainedmachine-learning model can analyze the new data and provide a resultthat includes a classification of the new data into a particular class,a clustering of the new data into a particular group, a prediction basedon the new data, or any combination of these.

In block 1114, the result is post-processed. For example, the result canbe added to, multiplied with, or otherwise combined with other data aspart of a job. As another example, the result can be transformed from afirst format, such as a time series format, into another format, such asa count series format. Any number and combination of operations can beperformed on the result during post-processing.

A more specific example of a machine-learning model is the neuralnetwork 1200 shown in FIG. 12. The neural network 1200 is represented asmultiple layers of interconnected neurons, such as neuron 1208, that canexchange data between one another. The layers include an input layer1202 for receiving input data, a hidden layer 1204, and an output layer1206 for providing a result. The hidden layer 1204 is referred to ashidden because it may not be directly observable or have its inputdirectly accessible during the normal functioning of the neural network1200. Although the neural network 1200 is shown as having a specificnumber of layers and neurons for exemplary purposes, the neural network1200 can have any number and combination of layers, and each layer canhave any number and combination of neurons.

The neurons and connections between the neurons can have numericweights, which can be tuned during training. For example, training datacan be provided to the input layer 1202 of the neural network 1200, andthe neural network 1200 can use the training data to tune one or morenumeric weights of the neural network 1200. In some examples, the neuralnetwork 1200 can be trained using backpropagation.

Backpropagation can include determining a gradient of a particularnumeric weight based on a difference between an actual output of theneural network 1200 and a desired output of the neural network 1200.Based on the gradient, one or more numeric weights of the neural network1200 can be updated to reduce the difference, thereby increasing theaccuracy of the neural network 1200. This process can be repeatedmultiple times to train the neural network 1200. For example, thisprocess can be repeated hundreds or thousands of times to train theneural network 1200.

In some examples, the neural network 1200 is a feed-forward neuralnetwork. In a feed-forward neural network, every neuron only propagatesan output value to a subsequent layer of the neural network 1200. Forexample, data may only move one direction (forward) from one neuron tothe next neuron in a feed-forward neural network.

In other examples, the neural network 1200 is a recurrent neuralnetwork. A recurrent neural network can include one or more feedbackloops, allowing data to propagate in both forward and backward throughthe neural network 1200. This can allow for information to persistwithin the neural network. For example, a recurrent neural network candetermine an output based at least partially on information that therecurrent neural network has seen before, giving the recurrent neuralnetwork the ability to use previous input to inform the output.

In some examples, the neural network 1200 operates by receiving a vectorof numbers from one layer; transforming the vector of numbers into a newvector of numbers using a matrix of numeric weights, a nonlinearity, orboth; and providing the new vector of numbers to a subsequent layer ofthe neural network 1200. Each subsequent layer of the neural network1200 can repeat this process until the neural network 1200 outputs afinal result at the output layer 1206. For example, the neural network1200 can receive a vector of numbers as an input at the input layer1202. The neural network 1200 can multiply the vector of numbers by amatrix of numeric weights to determine a weighted vector. The matrix ofnumeric weights can be tuned during the training of the neural network1200. The neural network 1200 can transform the weighted vector using anonlinearity, such as a sigmoid tangent or the hyperbolic tangent. Insome examples, the nonlinearity can include a rectified linear unit,which can be expressed using the following equation:y=max(x,0)where y is the output and x is an input value from the weighted vector.The transformed output can be supplied to a subsequent layer, such asthe hidden layer 1204, of the neural network 1200. The subsequent layerof the neural network 1200 can receive the transformed output, multiplythe transformed output by a matrix of numeric weights and anonlinearity, and provide the result to yet another layer of the neuralnetwork 1200. This process continues until the neural network 1200outputs a final result at the output layer 1206.

Other examples of the present disclosure may include any number andcombination of machine-learning models having any number and combinationof characteristics. The machine-learning model(s) can be trained in asupervised, semi-supervised, or unsupervised manner, or any combinationof these. The machine-learning model(s) can be implemented using asingle computing device or multiple computing devices, such as thecommunications grid computing system 400 discussed above.

Implementing some examples of the present disclosure at least in part byusing machine-learning models can reduce the total number of processingiterations, time, memory, electrical power, or any combination of theseconsumed by a computing device when analyzing data. For example, aneural network may more readily identify patterns in data than otherapproaches. This may enable the neural network to analyze the data usingfewer processing cycles and less memory than other approaches, whileobtaining a similar or greater level of accuracy.

Some machine-learning approaches may be more efficiently and quicklyexecuted and processed with machine-learning specific processors (e.g.,not a generic CPU). Such processors may also provide an energy savingswhen compared to generic CPUs. For example, some of these processors caninclude a graphical processing unit (GPU), an application-specificintegrated circuit (ASIC), a field-programmable gate array (FPGA), anartificial intelligence (AI) accelerator, a neural computing core, aneural computing engine, a neural processing unit, a purpose-built chiparchitecture for deep learning, and/or some other machine-learningspecific processor that implements a machine learning approach or one ormore neural networks using semiconductor (e.g., silicon (Si), galliumarsenide (GaAs)) devices. Furthermore, these processors may also beemployed in heterogeneous computing architectures with a number of and avariety of different types of cores, engines, nodes, and/or layers toachieve various energy efficiencies, thermal processing mitigation,processing speed improvements, data communication speed improvements,and/or data efficiency targets and improvements throughout various partsof the system when compared to a homogeneous computing architecture thatemploys CPUs for general purpose computing.

FIG. 13 shows a block diagram of an example of a system 1300 forhandling bulk requests and ad-hoc requests according to some aspects ofthe present disclosure. The system 1300 includes a service provider1302, such as a cloud computing provider or hotel. The service provider1302 can offer products or services as units 1340 to one or moreentities 1304 a-b. The entities 1304 a-b can have computing devices 1306a-b operable to submit requests for units 1340 to the service provider1302. Examples of the computing device 1306 a-b can include desktopcomputers, laptop computers, tablets, and mobile phones. The computingdevices 1306 a-b can transmit the requests to the service provider'scomputing system 1310 via one or more networks 1308, such as a localarea network or the Internet. The service provider's computing system1310 may include one or more computing devices, such as processors,servers, etc., capable of receiving and handling such requests.

In some examples, an entity 1304 a can submit a bulk request 1334 to thecomputing system 1310 of the service provider 1302. The bulk request1334 can be for reserving a group of units 1340 during a selectedtimeframe, such as from Dec. 21, 2022 to Dec. 25, 2022. The units 1340may be, for example, virtual machines or instances of containerizedapplications. The service provider 1302 can receive the bulk request1334 and assign a value to a parameter associated with the bulk request1334. This can be referred to as a bulk request parameter-value 1316.Examples of the parameter can include a quality of service (QoS)parameter, a latency parameter, a cost parameter, etc. The value may benegotiated by the entity 1304 a with the service provider 1302. Adesirable value may be settled upon as a reward for submitting the bulkrequest 1334.

In some cases, the system 1300 may use the bulk request parameter-value1316 to constrain a unit parameter-value, which is a parameter valueassigned to the individual units 1340 during the selected timeframe. Asone specific example in which the parameter is a latency parameter, thebulk request parameter-value 1316 can be a particular latency value thatserves as a constraint on the latency values assigned to individualunits during the selected timeframe. For instance, the particularlatency value assigned to the bulk request 1334 can serve as a minimumlatency value for the individual units during the selected timeframe.This may result from the service provider 1302 guaranteeing entity 1304a that the latency values assigned to the individual units will be noless than the bulk request parameter-value 1316.

In some situations, using the bulk request parameter-value 1316 toconstrain the unit parameter-value can be detrimental. For example, thismay lead to fewer entities issuing requests for units 1340 during theselected timeframe. Fewer entities issuing requests may lead tounderutilization of the units 1340 during the selected timeframe, amongother negative consequences. It may therefore be desirable in somesituations to adjust the bulk request parameter-value 1316 to achieve abetter result. To do so, the computing system 1310 can execute softwareconfigured to assist the service provider 1302 in determining whether,and how much, to adjust the bulk request parameter-value 1316. Thesoftware can execute the process shown in FIG. 14, which is furtherdescribed below.

Referring now to FIG. 14, shown is a flow chart of an example of aprocess for adjusting a bulk request parameter-value according to someaspects of the present disclosure. Other examples may include moreoperations, fewer operations, different operations, or a different orderof operations than is shown in FIG. 14. The operations for FIG. 14 aredescribed below with reference to the components of FIG. 13 describedabove.

In general, blocks 1402-1410 can be conceptualized as an initializationprocess for setting up the variable values used in the iterative processof block 1412. The iterative process of block 1412 is described ingreater detail later on with respect to subsequent figures.

Referring now to block 1402, the computing system 1310 of the serviceprovider 1302 sets a boundary reference value 1312 to zero. The boundaryreference value 1312 is a variable value stored in memory for use in theiterative process of block 1412 as a point of comparison. The boundaryreference value 1312 is designated as P* herein. Although in thisexample the boundary reference value 1312 is set to zero, in otherexamples the processor may set the boundary reference value 1312 to adefault value other than zero.

In block 1404, the computing system 1310 sets a gain reference value1314 to zero. The gain reference value 1314 is a variable value storedin memory for use in the iterative process of block 1412 as a point ofcomparison. The gain reference value 1314 is designated as Π* herein.Although in this example the gain reference value 1314 is set to zero,in other examples the processor may set the gain reference value 1314 toa default value other than zero.

In block 1406, the computing system 1310 determines a bulk requestparameter-value 1316 associated with a bulk request 1334. The bulkrequest 1334 is a request to access a group of units 1340 during aselected timeframe, which may for example span one day or multiple days.The bulk request parameter-value 1316 is a value for a particularparameter (PAR), where the value for the parameter is assigned to thebulk request 1334. The bulk request parameter-value 1316 is designatedas P₀ herein.

Upon receiving the bulk request 1334 from an entity 1304 a, a serviceprovider 1302 may assign the bulk request parameter-value 1316 to thebulk request 1334. This value may be negotiated by the entity 1304 awith the service provider 1302 or prefixed by the service provider 1302.

In block 1408, the computing system 1310 predicts a baseline benefitvalue 1318. The baseline benefit value 1318 can quantify an amount ofbenefit to the service provider 1302 in the scenario in which the bulkrequest parameter-value 1316 is used to constrain a unit parameter-value1322. The unit parameter-value 1322 can be another value for theparticular parameter (PAR) assigned to an individual unit during theselected timeframe. An example of the baseline benefit value may be abaseline profit amount. The baseline benefit value 1318 is designated asR₀ herein.

To generate this prediction, the computing system 1310 can execute abenefit computation engine 1328. The benefit computation engine 1328 issoftware that may include one or more models (e.g., machine-leaningmodels) configured to receive a unit-parameter value as input andprovide a corresponding benefit value as output. To determine thebaseline benefit value, the computing system 1310 can provide the bulkrequest parameter-value 1316 as the input to the benefit computationengine 1328. This targets the situation in which the unit-parametervalue is the same as the bulk request parameter-value 1316.

In some examples, the benefit computation engine 1328 can include one ormore algorithms (e.g., model) for predicting a benefit value based anynumber and combination of factors. Examples of these factors can includedetails of one or more ad-hoc requests, details of one or more bulkrequests, an amount of available units in the system, one or moreconstraints on the unit parameter-value, or any combination of these. Asone example in the context of a cloud computing environment, the benefitcomputation engine 1328 can compute an aggregate gain value given a listof ad-hoc requests submitted to the cloud computing environment andtheir associated unit parameter-values, a list of bulk requestssubmitted to the cloud computing environment and their associated bulkrequest parameter-values, and the availability of computing resources inthe cloud computing environment.

In block 1410, the computing system 1310 determines a lower boundaryconstraint 1320 on the unit parameter-value 1322. The lower boundaryconstraint 1320 can be a minimum value for the unit parameter-value1322, where the minimum value is determined independently of the bulkrequest parameter-value 1316. In other words, the computing system 1310can determine the lower boundary constraint 1320 based on factors otherthan the bulk request parameter-value 1316. The lower boundaryconstraint 1320 is designated as P_(k) herein.

To determine the lower boundary constraint 1320, the computing system1310 can execute a boundary identification engine 1330. The boundaryidentification engine 1330 is software that may include one or moremodels configured to receive one or more input values and provide thelower boundary constraint 1320 as output.

In some examples, the boundary identification engine 1330 can accept alist of bulk requests and their corresponding bulk requestparameter-values as input. The boundary identification engine 1330 canmay also accept one or more constraints (e.g., system-wide constraints)as input. Based on these inputs, the boundary identification engine 1330can identify the lower boundary constraint. The lower boundaryconstraint can be determined by removing the target bulk request 1334and its corresponding bulk request parameter-value 1316 (P₀) from thelist. More specifically, there may be multiple bulk requests submittedto the computing system 1310, one of which may be the target bulkrequest 1334. Each of the bulk requests may have a corresponding bulkrequest parameter-value. The boundary identification engine 1330 canreceive those bulk request parameter-values along with one or moreuser-designated constraints as input. Based on these inputs, theboundary identification engine 1330 can determine the lower boundaryconstraint by excluding the target bulk request 1334 and itscorresponding bulk request parameter-value 1316 from its computations.In this way, the boundary identification engine 1330 can determine whatthe lower boundary constraint would be if the target bulk request 1334was not a factor. That is, the boundary identification engine 1330 candetermine the lower boundary constraint independently of the target bulkrequest 1334 and based on a remainder of the bulk requests.

In block 1412, the computing system 1310 executes an iterative processusing the baseline benefit value 1318 and the lower boundary constraint1320. This iterative process 1326 is described in greater detail lateron with respect to FIG. 15. But generally, the iterative process caninvolve repeatedly updating the gain reference value 1314 and boundaryreference value 1312 if certain conditions are satisfied, until finalvalues for the gain reference value 1314 and the boundary referencevalue 1312 are obtained.

In block 1414, the computing system 1310 determines whether and how muchthe bulk request parameter-value 1316 should be adjusted. For example,the computing system 1310 can determine whether the gain reference value1314 is greater than zero following the iterative process 1326. If not,it may mean that the bulk request parameter-value 1316 should not beadjusted. Otherwise, it may mean that the bulk request parameter-value1316 should be adjusted. In some examples, the bulk requestparameter-value 1316 may be adjusted to be equivalent to the boundaryreference value 1312 stored in memory following the iterative process1326.

In block 1416, the computing system 1310 generates a recommendation orautomatically adjusts the bulk request parameter-value 1316. Forexample, the computing system 1310 can generate a graphical userinterface through which the user can select the bulk request 1334 orinput information about the bulk request 1334. The user may then selecta graphical object, such as a button, to initiate the iterative process1326. After completing the iterative process 1326, the computing system1310 can update the graphical user interface to include a graphicalrecommendation. The recommendation may indicate that the user shouldmodify the bulk request parameter-value 1316, or not modify the bulkrequest parameter-value 1316, depending on the results of the iterativeprocess 1326.

FIG. 15 is a flow chart of an example of the iterative process 1326according to some aspects of the present disclosure. Other examples mayinclude more operations, fewer operations, different operations, or adifferent order of operations than is shown in FIG. 15. The operationsfor FIG. 15 are described below with reference to the components ofFIGS. 13-14 described above.

In block 1502, the computing system 1310 determines a candidateparameter-value that respects the lower boundary constraint 1320. Thecandidate parameter-value is selected by computing system 1310 forevaluation during the current iteration of the iterative process 1326.The candidate parameter-value is a potential value for the particularparameter (PAR) described above with respect to FIG. 14, where thepotential value may be assigned to the individual units during theselected timeframe. Generally, the candidate parameter-value selectedduring each iteration is a new value relative to previous iterations ofthe iterative process 1326, in the sense that it has not been previouslyevaluated during prior iterations of the iterative process 1326. Thecandidate value is designated as T_(k) herein, where k can be thecurrent iteration number in the iterative process.

To determine the candidate parameter-value, the computing system 130 canexecute a parameter computation engine 1332. The parameter computationengine 1332 is software that may include one or more models configuredto receive the lower boundary constraint 1320 as an input and providethe candidate parameter-value as output.

In some examples, the parameter computation engine 1332 can include oneor more algorithms for predicting a unit parameter-value based anynumber and combination of factors. Examples of these factors can includedetails of one or more ad-hoc requests, details of one or more bulkrequests, an amount of available units in the system, one or moreconstraints on the unit parameter-value, or any combination of these. Asone example in the context of a cloud computing environment, theparameter computation engine 1332 can compute a unit parameter-valuegiven a list of ad-hoc requests submitted to the cloud computingenvironment and their associated unit parameter-values, a list of bulkrequests submitted to the cloud computing environment and theirassociated bulk request parameter-values, the availability of computingresources in the cloud computing environment, and one or moreconstraints on the unit parameter-value provided by a user.

In block 1504, the computing system 1310 determines a new benefit valuebased on the candidate parameter-value (T_(k)). The new benefit valuecan quantify an amount of benefit to the service provider 1302 in thescenario in which the lower boundary constraint 1320 (P_(k)) is used,instead of the bulk request parameter-value 1316, to constrain the unitparameter-value during the selected timeframe. The new benefit value isdesignated as R_(k) herein.

To make this determination, in some examples the computing system 1310can execute the benefit computation engine 1328. As noted above, thebenefit computation engine 1328 is software that can receive a unitparameter-value as input and provide a benefit value as output. Todetermine the new benefit value, the computing system 1310 can providethe new unit parameter-value as input to the benefit computation engine1328.

In block 1506, the computing system 1310 determines a new gain valuebased on the new benefit value and the baseline benefit value 1318. Thenew gain value is designated as Π_(k) herein. In some examples, thecomputing system 130 can determine the new gain value using thefollowing equation:Π_(k)=(R _(k) −R ₀)−G(P ₀ −P _(k))where R_(k) is new benefit value, R₀ is the baseline benefit value 1318,P₀ is the bulk request parameter-value 1316, P_(k) is lower boundaryconstraint 1320, and G is a total number of units associated with thebulk request 1334. Other examples may compute the new gain value usingmore, fewer, or different factors.

In block 1508, the computing system 1310 determines whether the new gainvalue (Π_(k)) is greater than the gain reference value 1314 (Π*). If so,the process continues to block 1510. If not, the process continues toblock 1514.

In block 1510, the computing system 1310 sets the gain reference valueto the new gain value. This involves updating the gain reference valuein memory to the new gain value.

In block 1512, the computing system 1310 sets the boundary referencevalue 1312 (P*) to the candidate parameter-value (T_(k)). This involvesupdating the boundary reference value in memory to the candidateparameter-value.

In block 1514, the computing system 1310 determines whether a stoppingcondition is satisfied. One example of the stopping condition caninclude the new value being greater than or equal to a threshold value1324. An example of the threshold value 1324 can be (P₀−ε₁), where ε₁ isa step value that may be predefined. If the stopping condition issatisfied, the iterative process can end. Otherwise, the process canproceed to block 1516, where the computing system 1310 updates the lowerboundary constraint 1320 based on the candidate parameter-value (T_(k)).For example, the computing system 1310 can update the lower boundaryconstraint 1320 to be equal to the candidate parameter-value plus ε₂,where ε₂ is a step value that may be predefined. It will be appreciatedthat ε₂ may be the same as or different from ε₁. After updating thelower boundary constraint 1320, the process may then return back toblock 1502 and iterate.

In some examples, the system can include a graphical user interface toassist a user in determining whether, and how much, to adjust a bulkrequest parameter-value. One example of the graphical user interface1600 is shown in FIG. 16. As shown, the graphical user interface 1600can include various graphical objects (e.g., objects 1604 a-e, 1606,1610, and 1612) for inputting or outputting information. Examples ofsuch graphical objects can include drop-down menus, text boxes, radiobuttons, check boxes, tables, buttons, etc.

In particular, the graphical user interface 1600 can include objects1604 a-e. Object 1604 a can allow a user to select which type ofevaluation to perform. Here, the user has selected that they would liketo evaluate an existing bulk request, but another option may be toevaluate a new bulk request. Object 1604 b can allow the user to searchfor bulk requests by date. Object 1604 c can allow the user to input thename of a bulk request to be evaluated. Object 1604 d can allow the userto input, or can output, a time duration associated with a selected bulkrequest. One example of the time duration can be five days, for instanceif the bulk request is a reservation to use a group of virtual machinesfor a five-day timespan. Object 1604 e can allow the user to input, orcan output, a starting date associated with the bulk request. Thestarting date can be the date at which the time duration begins.

The graphical user interface 1600 can also include a table 1606providing information about the timeframe associated with the bulkrequest. In the example shown in FIG. 16, the bulk request has a startdate of Dec. 11, 2022 and spans for five days, so the table 1606 spansfrom Dec. 11, 2022 to Dec. 15, 2022. Each row in the table 1606corresponds to a different date and can provide information about theunits with respect to that date.

In the table 1606, the column labeled “Current Value” indicates the bulkrequest parameter-value as currently set for a given date. The bulkrequest parameter-values shown in the “Current Value” column mayconstrain the unit parameter-values associated with the dates shown inthe table 1606. In the last three columns of the table 1606, the usercan input alternative/optional values for the bulk requestparameter-value on each date. Of course, in other examples that table1606 may include more or fewer columns to allow the user to input moreor fewer optional values, respectively. These inputs may be received intext boxes 1608, which are incorporated into the table 1606.

Once the user has input the optional values, the user can press a button1610 for causing the computing system 1310 to estimate gain valuesassociated with the alternative values. The estimated gains may then beoutput in a results portion 1612. As shown in FIG. 16, the resultsportion 1612 can provide an expected gain increase or decreasecorresponding to each of the optional values input by the user. Anindicator 1614 can indicate which of the three optional values yieldsthe best result (e.g., the highest gain). If the indicator 1614indicates that one of the optional values yields more gain than thecurrent values, it may suggest to the user that the bulk requestparameter-value should be adjusted, for example to the optional valuethat yielded the gain improvement. To make this more explicit, in someexamples the graphical user interface 1600 may output a recommendationto adjust (e.g., lower) the bulk request parameter-value to the optionalvalue that yielded the gain improvement. In this way, the user canmanually input optional values for the bulk request parameter-values onvarious dates and receive automated assessments of those optional valuesas outputs in the graphical user interface 1600.

Although the graphical user interface 1600 includes a certain number andarrangement of graphical elements, this is for illustrative purposes andnot intended to be limiting. Other examples may include more graphicalelements, fewer graphical elements, different graphical elements, or adifferent arrangement of the graphical elements shown in FIG. 16.

FIG. 17 is a flow chart of an example of a process for facilitating thecreation and usage of an interactive user interface, such as the GUI1600 of FIG. 16, according to some aspects of the present disclosure.Other examples may include more operations, fewer operations, differentoperations, or a different order of operations than is shown in FIG. 17.The operations for FIG. 17 are described below with reference to thecomponents of FIGS. 13 and 16 described above.

In block 1702, a computing system 1310 generates an interactive userinterface. The interactive user interface can be a graphical userinterface for display on a display device. A system operator or otheruser may be able to interact with the user interface to determinewhether, and how much, to adjust a bulk request parameter-value.

In block 1704, the computing system 1310 receives a selection of a bulkrequest as input from a user a via the interactive user interface. Forexample, the user may select a desired bulk request from a dropdown listof bulk requests or input a name of the desired bulk request into atextbox.

In block 1706, the computing system 1310 receives a target value for abulk request parameter-value as input from the user via the interactiveuser interface. For example, the user may input the target value into atext box, similar to how the optional value is input into the text box1608 of FIG. 16.

In block 1708, the computing system 1310 determines an estimated gainvalue associated with the target value based on a baseline benefit value1318. The baseline benefit value 1318 can be determined with respect tothe selected bulk request by implementing the operations of block 1408.In some examples, the estimated gain can be determined by implementingthe operations of blocks 1504-1506 of FIG. 15, with the target valueserving as the candidate parameter-value. The estimated gain value isdesignated as E herein.

In block 1710, the computing system 1310 determines whether theestimated gain value is greater than the gain reference value 1314,which may be zero. If so, the process can proceed to block 1712.Otherwise, the process can proceed to block 1714.

In block 1712, the computing system 1310 generates a first output in theinteractive user interface recommending that the bulk requestparameter-value 1316 associated with the selected bulk request beadjusted. The first output may be a graphical output, for example apopup notification or icon.

In block 1714, the computing system 1310 generates a second output inthe interactive user interface recommending that the bulk requestparameter-value 1316 associated with the selected bulk request bemaintained as is. The second output may be a graphical output, forexample a popup notification or icon.

As described above, some examples of the present disclosure may beapplicable to computer networks and other contexts in which bulkrequestors are provided with guarantees that impact ad-hoc requests.Another example of an applicable context can be supply chain management(SCM), in which entities can submit bulk requests and ad-hoc requestsfor items, of which there may be a fixed and limited supply. A provider(e.g., manufacturer) of the items may have relationships with the bulkrequestors and provide guarantees thereto of the types described above.Those guarantees may constrain a unit parameter-value (e.g., a price)associated with the items when they are requested on an ad-hoc basis.Some examples of the present disclosure can help the item providerdetermine whether and how much to adjust a bulk-request parameter valueassociated with a bulk request for the items, so as to achieve a desiredresult.

In the previous description, for the purposes of explanation, specificdetails are set forth in order to provide a thorough understanding ofexamples of the technology. But various examples can be practicedwithout these specific details. The figures and description are notintended to be restrictive.

The previous description provides examples that are not intended tolimit the scope, applicability, or configuration of the disclosure.Rather, the previous description of the examples provides those skilledin the art with an enabling description for implementing an example.Various changes may be made in the function and arrangement of elementswithout departing from the spirit and scope of the technology as setforth in the appended claims.

Specific details are given in the previous description to provide athorough understanding of the examples. But the examples may bepracticed without these specific details. For example, circuits,systems, networks, processes, and other components can be shown ascomponents in block diagram form to prevent obscuring the examples inunnecessary detail. In other examples, well-known circuits, processes,algorithms, structures, and techniques may be shown without unnecessarydetail in order to avoid obscuring the examples.

Also, individual examples may have been described as a process that isdepicted as a flowchart, a flow diagram, a data flow diagram, astructure diagram, or a block diagram. Although a flowchart can describethe operations as a sequential process, many of the operations can beperformed in parallel or concurrently. In addition, the order of theoperations can be re-arranged. And a process can have more or feweroperations than are depicted in a figure. A process can correspond to amethod, a function, a procedure, a subroutine, a subprogram, etc. When aprocess corresponds to a function, its termination can correspond to areturn of the function to the calling function or the main function.

Systems depicted in some of the figures can be provided in variousconfigurations. In some examples, the systems can be configured as adistributed system where one or more components of the system aredistributed across one or more networks in a cloud computing system.

The invention claimed is:
 1. A system comprising: one or moreprocessors; and one or more memory devices including program code thatis executable by the one or more processors for causing the one or moreprocessors to: set a boundary reference value to a first default valuein memory; set a gain reference value to a second default value in thememory; determine a bulk request parameter-value assigned to a bulkrequest, wherein the bulk request is for reserving a group of unitsduring a selected timeframe, and wherein the bulk requestparameter-value is a value for a parameter assigned to the bulk request;predict a baseline benefit value based on the bulk requestparameter-value, the baseline benefit value being predicted by using thebulk request parameter-value as a lower boundary for a unitparameter-value, the unit parameter-value being another value for theparameter assigned to an individual unit during the selected timeframe;determine a lower boundary constraint for the unit parameter-valueindependently of the bulk request parameter-value; execute an iterativeprocess including: a) determining a candidate parameter-value for theparameter based on the lower boundary constraint; b) determining a newgain value based on the candidate parameter-value and the baselinebenefit value; c) in response to determining that the new gain value isgreater than the gain reference value: setting the gain reference valueto the new gain value; and setting the boundary reference value to thelower boundary constraint; d) in response to determining that thecandidate parameter-value is less than a threshold value: updating thelower boundary constraint based on the candidate parameter-value; andreturning to operation (a); and e) in response to determining that thecandidate parameter-value is greater than or equal to the thresholdvalue: exiting the iterative process; subsequent to exiting theiterative process, determine whether the gain reference value is greaterthan zero; and based on determining that the gain reference value isgreater than zero, adjust the bulk request parameter-value to theboundary reference value.
 2. The system of claim 1, wherein the one ormore memory devices further include program code that is executable bythe one or more processors for causing the one or more processors to,subsequent to exiting the iterative process: determine whether the gainreference value is less than or equal to zero; and based on determiningthat the gain reference value is less than or equal to zero, generate anoutput recommending that the bulk request parameter-value be maintained.3. The system of claim 1, wherein the threshold value is equal to thebulk request parameter-value minus a step value.
 4. The system of claim1, wherein the updating the lower boundary constraint based on thecandidate parameter-value includes: updating the lower boundaryconstraint to be equivalent to the candidate parameter-value plus a stepvalue.
 5. The system of claim 1, wherein the one or more memory devicesfurther include program code that is executable by the one or moreprocessors for causing the one or more processors to, for each iterationof the iterative process: determine a new benefit value for a currentiteration based on the candidate parameter-value for the currentiteration; determine a first computed value by subtracting the baselinebenefit value from the new benefit value; determine a second computedvalue by subtracting a current value of the lower boundary constraintfor the current iteration from an initial value of the lower boundaryconstraint generated prior to initiating the iterative process;determine a third computed value by multiplying the second computedvalue by a number of units in the group of units; and determine the newgain value by subtracting the third computed value from the firstcomputed value.
 6. The system of claim 1, wherein the group of units arecomputer processors and the individual unit is an individual computerprocessor.
 7. The system of claim 6, wherein the parameter is abandwidth parameter, memory consumption parameter, processor consumptionparameter, quality of service parameter, latency parameter, or a costparameter.
 8. The system of claim 1, wherein the group of units arephysical rooms and the individual unit is an individual physical room.9. The system of claim 8, wherein the parameter is a cost parameter. 10.The system of claim 1, wherein the one or more memory devices furtherinclude program code that is executable by the one or more processorsfor causing the one or more processors to: generate an interactive userinterface including an interface component through which a user caninput a target value for the bulk request parameter-value; receive thetarget value from the user via the interactive user interface; determinean estimated gain value based on the target value and the baselinebenefit value; determine whether the estimated gain value is greaterthan the gain reference value; in response to determining that theestimated gain value is greater than the gain reference value, generatea first output in the interactive user interface recommending that thebulk request parameter-value be adjusted to the target value; and inresponse to determining that the estimated gain value is less than orequal to the gain reference value, generate a second output in theinteractive user interface recommending that the bulk requestparameter-value be maintained as is.
 11. A method comprising: setting,by one or more processors, a boundary reference value to a first defaultvalue in memory; setting, by the one or more processors, a gainreference value to a second default value in the memory; determining, bythe one or more processors, a bulk request parameter-value assigned to abulk request, wherein the bulk request is for reserving a group of unitsduring a selected timeframe, and wherein the bulk requestparameter-value is a value for a parameter assigned to the bulk request;predicting, by the one or more processors, a baseline benefit valuebased on the bulk request parameter-value, wherein the baseline benefitvalue is predicted by using the bulk request parameter-value as a lowerboundary for a unit parameter-value, the unit parameter-value beinganother value for the parameter assigned to an individual unit duringthe selected timeframe; determining, by the one or more processors, alower boundary constraint for the unit parameter-value independently ofthe bulk request parameter-value; executing, by the one or moreprocessors, an iterative process including: a) executing a parametercomputation engine configured to determine a candidate parameter-valuefor the parameter based on the lower boundary constraint; b) determininga new gain value based on the candidate parameter-value and the baselinebenefit value; c) in response to determining that the new gain value isgreater than the gain reference value: setting the gain reference valueto the new gain value; and setting the boundary reference value to thelower boundary constraint; and d) in response to determining that thecandidate parameter-value is less than a threshold value: updating thelower boundary constraint based on the candidate parameter-value; andreturning to operation (a); exiting the iterative process; subsequent toexiting the iterative process, determining, by the one or moreprocessors, whether the gain reference value is greater than zero; andbased on determining that the gain reference value is greater than zero,adjusting, by the one or more processors, the bulk requestparameter-value to the boundary reference value.
 12. The method of claim11, further comprising: determine whether another gain reference valueassociated with another bulk request parameter-value is less than orequal to zero; and based on determining that the other gain referencevalue is less than or equal to zero, generate an output recommendingthat the other bulk request parameter-value be maintained.
 13. Themethod of claim 11, wherein the threshold value is equal to the bulkrequest parameter-value minus a step value.
 14. The method of claim 11,wherein the updating the lower boundary constraint based on thecandidate parameter-value includes: updating the lower boundaryconstraint to be equivalent to the candidate parameter-value plus a stepvalue.
 15. The method of claim 11, further comprising, for eachiteration of the iterative process: determining a new benefit value fora current iteration based on the candidate parameter-value for thecurrent iteration; determining a first computed value by subtracting thebaseline benefit value from the new benefit value; determining a secondcomputed value by subtracting a current value of the lower boundaryconstraint for the current iteration from an initial value of the lowerboundary constraint generated prior to initiating the iterative process;determining a third computed value by multiplying the second computedvalue by a number of units in the group of units; and determining thenew gain value by subtracting the third computed value from the firstcomputed value.
 16. The method of claim 11, wherein the group of unitsare computer processors and the individual unit is an individualcomputer processor.
 17. The method of claim 16, wherein the parameter isa bandwidth parameter, memory consumption parameter, processorconsumption parameter, quality of service parameter, latency parameter,or a cost parameter.
 18. The method of claim 11, wherein the group ofunits are physical rooms and the individual unit is an individualphysical room.
 19. The method of claim 18, wherein the parameter is acost parameter.
 20. The method of claim 11, further comprising:generating an interactive user interface including an interfacecomponent through which a user can input a target value for the bulkrequest parameter-value; receiving the target value from the user viathe interactive user interface; determining an estimated gain valuebased on the target value and the baseline benefit value; determinewhether the estimated gain value is greater than the gain referencevalue; in response to determining that the estimated gain value isgreater than the gain reference value, generate a first output in theinteractive user interface recommending that the bulk requestparameter-value be adjusted to the target value; and in response todetermining that the estimated gain value is less than or equal to thegain reference value, generate a second output in the interactive userinterface recommending that the bulk request parameter-value bemaintained as is.
 21. A non-transitory computer-readable mediumcomprising program code that is executable by one or more processors forcausing the one or more processors to: set a boundary reference value toa first default value in memory; set a gain reference value to a seconddefault value in the memory; determine a bulk request parameter-valueassigned to a bulk request, wherein the bulk request is for reserving agroup of units during a selected timeframe, and wherein the bulk requestparameter-value is a value for a parameter assigned to the bulk request;predict a baseline benefit value based on the bulk requestparameter-value, the baseline benefit value being predicted by using thebulk request parameter-value as a lower boundary for a unitparameter-value, the unit parameter-value being another value for theparameter assigned to an individual unit during the selected timeframe;determine a lower boundary constraint for the unit parameter-valueindependently of the bulk request parameter-value; execute an iterativeprocess including: a) executing a parameter computation engineconfigured to determine a candidate parameter-value for the parameterbased on the lower boundary constraint; b) determining a new gain valuebased on the candidate parameter-value and the baseline benefit value;c) in response to determining that the new gain value is greater thanthe gain reference value: setting the gain reference value to the newgain value; and setting the boundary reference value to the lowerboundary constraint; d) in response to determining that the candidateparameter-value is less than a threshold value: updating the lowerboundary constraint based on the candidate parameter-value; andreturning to operation (a); and e) in response to determining that thecandidate parameter-value is greater than or equal to the thresholdvalue: exiting the iterative process; subsequent to exiting theiterative process, determine whether the gain reference value is greaterthan zero; and based on determining that the gain reference value isgreater than zero, adjust the bulk request parameter-value to theboundary reference value.
 22. The non-transitory computer-readablemedium of claim 21, further comprising program code that is executableby the one or more processors for causing the one or more processors to,subsequent to exiting the iterative process: determine whether the gainreference value is less than or equal to zero; and based on determiningthat the gain reference value is less than or equal to zero, generate anoutput recommending that the bulk request parameter-value be maintained.23. The non-transitory computer-readable medium of claim 21, wherein thethreshold value is equal to the bulk request parameter-value minus astep value.
 24. The non-transitory computer-readable medium of claim 21,wherein the updating the lower boundary constraint based on thecandidate parameter-value includes: updating the lower boundaryconstraint to be equivalent to the candidate parameter-value plus a stepvalue.
 25. The non-transitory computer-readable medium of claim 21,further comprising program code that is executable by the one or moreprocessors for causing the one or more processors to, for each iterationof the iterative process: determine a new benefit value for a currentiteration based on the candidate parameter-value for the currentiteration; determine a first computed value by subtracting the baselinebenefit value from the new benefit value; determine a second computedvalue by subtracting a current value of the lower boundary constraintfor the current iteration from an initial value of the lower boundaryconstraint generated prior to initiating the iterative process;determine a third computed value by multiplying the second computedvalue by a number of units in the group of units; and determine the newgain value by subtracting the third computed value from the firstcomputed value.
 26. The non-transitory computer-readable medium of claim21, wherein the group of units are computer processors and theindividual unit is an individual computer processor.
 27. Thenon-transitory computer-readable medium of claim 26, wherein theparameter is a bandwidth parameter, memory consumption parameter,processor consumption parameter, quality of service parameter, latencyparameter, or a cost parameter.
 28. The non-transitory computer-readablemedium of claim 21, wherein the group of units are physical rooms andthe individual unit is an individual physical room.
 29. Thenon-transitory computer-readable medium of claim 28, wherein theparameter is a cost parameter.
 30. The non-transitory computer-readablemedium of claim 21, further comprising program code that is executableby the one or more processors for causing the one or more processors to:generate an interactive user interface including an interface componentthrough which a user can input a target value for the bulk requestparameter-value; receive the target value from the user via theinteractive user interface; determine an estimated gain value based onthe target value and the baseline benefit value; determine whether theestimated gain value is greater than the gain reference value; inresponse to determining that the estimated gain value is greater thanthe gain reference value, generate a first output in the interactiveuser interface recommending that the bulk request parameter-value beadjusted to the target value; and in response to determining that theestimated gain value is less than or equal to the gain reference value,generate a second output in the interactive user interface recommendingthat the bulk request parameter-value be maintained as is.